{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[字体设置] 使用自定义字体: SimHei\n",
      "=== 自闭症分类分析开始 ===\n",
      "[数据加载] ASD样本：124，TD样本：145\n",
      "[数据维度] 特征矩阵：(269, 23)\n",
      "[内存监控] 加载数据后: 467.36 MB\n",
      "[数据集划分] 训练集：215，测试集：54\n",
      "[内存监控] 数据划分后: 467.36 MB\n",
      "\n",
      "=== 超参数调优 ===\n",
      "[内存监控] 调优开始前: 467.36 MB\n",
      "\n",
      "[调优开始] 随机森林\n",
      "[调优完成] 随机森林 - 最佳参数: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "[调优完成] 随机森林 - 最佳F1分数: 0.6924\n",
      "[调优时间] 随机森林: 3.86秒\n",
      "\n",
      "[调优开始] 逻辑回归\n",
      "[调优完成] 逻辑回归 - 最佳参数: {'C': 1.0, 'solver': 'liblinear'}\n",
      "[调优完成] 逻辑回归 - 最佳F1分数: 0.6996\n",
      "[调优时间] 逻辑回归: 0.11秒\n",
      "\n",
      "[调优开始] 支持向量机\n",
      "[调优完成] 支持向量机 - 最佳参数: {'C': 1.0, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "[调优完成] 支持向量机 - 最佳F1分数: 0.6991\n",
      "[调优时间] 支持向量机: 0.20秒\n",
      "\n",
      "[调优开始] K近邻\n",
      "[调优完成] K近邻 - 最佳参数: {'n_neighbors': 5, 'weights': 'uniform'}\n",
      "[调优完成] K近邻 - 最佳F1分数: 0.6891\n",
      "[调优时间] K近邻: 0.05秒\n",
      "\n",
      "=== 模型训练与评估 ===\n",
      "[内存监控] 训练开始前: 467.36 MB\n",
      "\n",
      "[训练开始] 随机森林\n",
      "[训练完成] 随机森林 - F1分数: 0.7407\n",
      "\n",
      "[训练开始] 逻辑回归\n",
      "[训练完成] 逻辑回归 - F1分数: 0.7692\n",
      "\n",
      "[训练开始] 支持向量机\n",
      "[训练完成] 支持向量机 - F1分数: 0.7843\n",
      "\n",
      "[训练开始] K近邻\n",
      "[训练完成] K近邻 - F1分数: 0.7170\n",
      "\n",
      "[训练开始] 集成模型\n",
      "[训练完成] 集成模型 - F1分数: 0.7692\n",
      "\n",
      "=== 多算法性能对比表 ===\n",
      "算法     准确率     精确率   召回率    F1分数  训练时间\n",
      " 随机森林  0.7407  0.6897  0.80  0.7407  0.06\n",
      " 逻辑回归  0.7778  0.7407  0.80  0.7692  0.00\n",
      "支持向量机  0.7963  0.7692  0.80  0.7843  0.01\n",
      "  K近邻  0.7222  0.6786  0.76  0.7170  0.00\n",
      " 集成模型  0.7778  0.7407  0.80  0.7692  0.12\n",
      "[可视化] 已保存模型性能对比图\n",
      "[可视化] 已保存ROC曲线图\n",
      "[可视化] 已保存混淆矩阵图\n",
      "\n",
      "=== 自闭症分类分析完成 ===\n",
      "分析结果已保存为图片文件\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iIkfkSU9n0333k7VwISYyktovvUjsJZc4HUskoHbsyKR790/4+ut1fPHFWr799lZHfjFRURMRkcPK27SJ1Lt6k7d+Pe64aiS8OZLIRg2djiUSULNmpXDLLR+xefN+qlSJ5L77Wjo2eqyiJiIih5XSKRnP7t2En3aab2Vn7dpORxIJGI/Hy5Ahcxg8eBZer+WCCxKZMuVGEhIqOpZJRU1ExCHWWnZl5hXLvqr5/9yZkVss+zvAs3s3oS1bETP0RdJjYqGY9++k3Hyv0xEkiHg8Xq688l2+/349xsATT7Rl8OALCQlx9gIZKmoiEnzevRn+mOF0ioD7cs8TbMhtXkx7+9j3x4B5xbQ/n6+TWjIi/gY8ry0s1v0Gg8iE8cSe9TsDl8LApU6nEae53S5atarN8uXbeOedG7jssvpORwJU1EQkGJWDkgYUY0kLjNz8Hbx7/i1UKqMrO/Nifnc6QolpW7ut0xGCUkGBl/Xr9xReC+3ppy/knntaEB8f43Cyv6moiUjwGpzudILA6jMTgP8b0oqEKlGORvFkZJD2wINkzp2LCQuj1n+GUuHKZB5yNFVgNZro+3NF9xXOBhFHpKam06XLh/z55x6WLetdePmNYCppoDsTiIiUe/lbtrDhlq5kzp2Lu0oVEidOoMKVVzodSyRgPv/8d5o0Gc28eam4XIa0tP1ORzosjaiJiJRj2StXsenuuynYsYOwevVIGD2KsIQEp2OJBERenoeBA7/n1VcXAHDVVacyceL1xMVFO5zs8FTURETKqf0zfyCtf39sdjZRLVpQ543XcVd07jIEIoG0fv0eOneezqJFmwkJcfH885fw0EOtcbmCew6mipqISDm0e9Jktr3wAni9VLzuOmo+9ywmLMzpWCIBs379HhYv3kxSUkWmTu1Iq1Z1nI50TFTURETKEevxsO35F9jzzjsAVLvvXqrdfbfu2SllktdrC0fMLr20HlOm3MQVV9SncuVIh5MdOy0mEBEpJ7yZmWy65172vPMOJjSUWi/+h7i+fVXSpExau3YX5503lh9++Kvwuc6dG5aqkgYqaiIi5UL+tu1s6HYbGT/8gLtiRRLHvU3FDh2cjiUSEO++u5xzzx3NL79s4emnf8Ra63SkE6ZTnyIiZVzO77+T2rsPBVu3EpqUSMKoUYSfcorTsUSKXVZWPvfe+xXjxi0DfCNoo0dfW6pHjVXURETKsIw5c0l74AG8mZlEnnsudUYMJ6RyZadjiRS7Vau206nTdFav3kFERAhvvHEVt9/etFSXNFBRExEps/ZMncbW554Dj4cKV19NzeeH4AoPdzqWSLHLz/dw7bXvkZKylzPPrMb773ekUaN4p2MVCxU1EZEyxnq9bH/pZXaPGwdA1T69ibvvPoxL05KlbAoNdTN69LW8995Khg+/iujosnOpGRU1EZEyxJuTw+ZHHmX/jBkQEkLNZ56h0k03Oh1LpNgtXbqFBQs2cffd5wFw+eX1ufzy+g6nKn4qaiIiZUTBzp2k9utHzq/LccXGUuf1YUS3bu10LJFiZa3lzTcX8dBDMygo8NK4cQ3atCm7tz1TURORoPXit785HeF//HfPENJylxbLvvowDICrP2950vuqvdPy2PseqqfD9orwfKcs0tbeBWtPetciQWPv3hxuv/0zPvpoDQB3392cpk1rOJwqsFTURCRojfjhT6cj/I/Ys4qnpBWnBileBnzkJToX/qgJ/7nZTXp06V7pVlLa1m7rdAQ5RgsXbqJz5w9JSdlLhQrhjB3bgU6dGjgdK+BU1EQkaF16VjxNEoLrJuGjU31/9k746OR39pNvZyu6rzjhXez96GO2fPAUFHiJvewyrv3PUP4vsnRdeV3kaKZPX02XLh9SUOClWbOaTJvWkfr1qzgdq0SoqIlI0LrozDi6tkxyOsY/jJ7o+/Oei0876X2NeD/1hN9rrWXH66+za+QoAKr06kX1Af21slPKpDZtEqhSJZJbbmnICy9cSnh4+akv5ecrFREpI7y5uWx5/An2ffkluN3UGPQklTt3djqWSLFaunQL55wTj9vtolatWFav7kvVqlFOxypx+tVLRKQUKdizh429bmffl1/iiooiYdRIlTQpUzweL//+92yaNx/LkCFzCp8vjyUNNKImIlJq5KWksLF3b/I3bCSkRg0SRo0k4swznY4lUmy2bs2gW7eP+f779QDk5nocTuQ8FTURkVIga/FiNvW7B096OuFnn0XCyFGExld3OpZIsfn++/XceutHbNuWSVxcFJMn38AVV5zqdCzH6dSniEiQS//8Czb27IUnPZ2YCy+k7uTJKmlSZhQUeBk0aCaXXz6Zbdsyueiiuixb1kclzU8jaiIiQcpay65Ro9gx7HUAKt96K/GPDcS43Q4nEyk+xsDcuakYYxg8uD1PPNEWt1vjSAeoqImIBCGbl8eWp54m/ZNPwBjiH3uMKrd1czqWSLEpKPASEuLC7Xbx7rs3snbtLi68sK7TsYKOipqISJDxpKez6b77yVq4EBMZSe2XXyL24oudjiVSLPLyPDzxxH/59ddtfP1118LLb9SqFet0tKCkoiYiEkTyUlNJ7d2HvPXrccdVI2HkKCIblv3b5Ej5kJKyl86dp7NwYRput2HhwrQyfUP14qCTwCIiQSJ72TJSkjuTt3494aefzinTpqmkSZnx0UdraNp0NAsXppGQUIHZs3uqpB0DjaiJiASBfd98y+ZHH8Xm5hJ9/vnUHvYa7pgYp2OJnLScnAIefngGw4cvAuD//u8Mxo+/jipVdE/aY6ERNRERh+16+23SHngAm5tLpU6dSBg1UiVNyoy33/6F4cMXERrq4rXXruCTT5JV0o6DRtRERBy2/cWXAKj+8ACq9OqFMcbhRCLFp3fv5vz882buvbcFzZvXcjpOqWOstU5nOG7Nmze3ixcvdjqGlGFfDP+VDSt3OR1DyolLfnqIWkOHUuHKK5yOInLSsrLyGTRoJo88cj7x8RoZBjDGLLHWNj+R92pETeQQVNKkpFTb9ztJEycQ2aSJ01FETtrq1Tvo1OkDVq3awR9/7Oazz7o4HanUU1ETOYJ+o8rutat2ZeRS9SXfbYgyHwu+YhrqdhEWEnzTaBtNbATAiu4rTuj92StXkXp3Hzw7dhJWrx4JY0YTVqdOcUYUKXHWWiZMWEa/fl+RnV3AGWdU5V//Krs/P0uSipqIEB2uHwUlYf/MmaT1H4DNziaqZUvqvD4Md8WKTscSOSkZGXn07fslkycvB+C22xozYsTVxMSEOZysbNBPZxGRErB70mS2Pf88WEvFG26g5jODMWH6h0xKt5ycAlq0GMuaNTuJigrlzTevpnt3ncYvTipqIiIBZD0etj3/AnveeQeAuPvvo2qfPlrZKWVCREQIN910Fp988jvvv9+Rs86KczpSmaOiJiISIN7MTNL6DyDjxx8xoaHUHDKEih2udTqWyEnZuzeHv/7aQ9OmNQF4+ukLefzxtkRGhjqcrGxSURMRCYD8bdtJvbsPuavX4K5YkTojhhPV/IRW54sEjUWL0khOnk52dgHLlvUmPj6GkBAXIUG48Kes0HdWRKSY5fz+OynJyeSuXkNoUiJ1p01VSZNSzVrLq6/+xPnnj+Ovv/ZSq1Ys2dkFTscqFzSiJiJSjDLmzCHt/gfwZmURee651BkxnJDKlZ2OJXLCdu3KomfPT/n887UA3H9/S4YOvZRwrRYvEfoui4gUkz1Tp7H1uefA46HCNddQc8i/cYWHOx1L5ITNn59KcvJ0Nm3aR6VKEYwffx3XX3+m07HKFRU1EZGTZL1etr/0MrvHjQOg6t19iLvvPq3slFIvKyuftLR9tGpVh6lTbyIpqZLTkcodFTURkZPgzc5m8yOPsv+77yAkhJrPPEOlm250OpbICcvJKSAiwlcPLr20Hl9/3ZWLLz6F0FC3w8nKJy0mEBE5QQU7d7Khew/2f/cdrthYEt8aq5ImpdrMmX9x6qmv88MPfxU+d8UVp6qkOUhFTUTkBOSuW0dKcmdyli8ntHZt6k59j+hWrZyOJXJCPB4vTz/9A5deOom0tP2MHr3E6Ujip1OfIiLHqWGKl5Tht+Ddv5+Ic84h4c0RhFSr5nQskROyefN+brnlQ2bN2oAx8NRT7Rg0qL3TscRPRU2knLJOByiFPBkZXLHYS/f/evF69xN7+eXUGvoCrshIp6OJnJBvvllHt24fs3NnFjVqxPDOOzdwySX1nI4lRaioSZnxxfBf2bByl9Mx/kffSa2YYzOdjnFopyT6/pzYyNkcwcpaEnZCkz8tTddbzky13O71bapyey+q9++PcWkGiZROmZl59OjxCTt3ZnHZZfWYPPkG4uNjnI4lB1FRkzKjuEtaUsOqxbKfoC1pckjheZZGKZam/nJWbd/f27wG1tSBjZecxQMPP+xcSJFiEB0dxqRJN7B48WYGDrwAl0uXkwlGKmpS5vQbdbHTEQ5pRfcVJX5Mr9cy648dTJiXwqy1Owqfb12vKj3Or8ulZ8XjLuc/nK215K1fT8as2WTMmU3W4iWQn1+43V2tGjEXXEBM+3ZEt2lDg4oVHUwrcnI+/fQ3/vxzDw891BqAyy+vz+WX13c4lRyJippIGZSRW8CHSzYxcX4K63f6RvTCQ1zc0LQ2Pc6vy5k1Kjic0FnerCwyFywkY85sMmfNJn/z5r83ulxENmniK2Zt2xFx9lk6vSmlXm5uAY8++j3Dhi3E5TJcfPEpNGlSw+lYcgxU1ETKkJSdmUz8KYUPFm8iI9d3w+RaFSPo1rounc9LoHJ0mLMBHWKtJe+vFDLnzCZj1myyFi3CFh01q1KFmLYXEN3ON2qme3NKWbJu3W6Sk6fzyy9bCA11MXTopTRuHO90LDlGKmoipZy1lrnrdjJ+Xgo//L4d61/O2aJuFXqcX5fLz44nxF3+RoS82dlkLlxI5uw5ZMyZQ35q6t8bjSGi8TnEtGtHTLt2RDRooFEzKZOmTVvJnXd+zv79eZxySiWmTu1Iixa1nY4lx0FFTaSUyswt4KOlaUycn8K67RkAhIW4uK5xLbq3qUvD2uVvLlVeSgoZs+eQMXs2WT//jM3LK9zmrlSJ6LZtiWnXlugLLtComZR5r7++kPvv/waAjh3PZuzYDlSqFOFwKjleKmoipUzq7iwm/ZTC1EWp7M/xnd6MrxBOt1ZJdGmRSNWYcGcDliBvTg5ZixYVLgTI37DxH9sjGjUipm1bYtq3I6JhQ4xbt8GR8qNjx7N56aX5PPbYBfTp0xxjyvfCodJKRU2kFLDW8tOfuxg/P4Xv12wrPL3ZLKkyPdrU5cqGNQgtJ6c381JT/16hufBnbE5O4TZXxYrEnH++byHABRcQUrV4LrEiUlp89dUfXHFFfdxuF7VqxbJ27b2FN1iX0kl/eyJBLDvPw8dL05gw/y/WbvOd3gx1Gzqc4zu92TihksMJA8+bm0vWosWFCwHyUlL+sT2iQQOi27Ulpl07Is85R6NmUi5lZOTRr99XTJr0K889dxFPPtkOQCWtDNDfoEgQ2rQni8kLNjD151TSs32rE+Niw7m1ZRJdWiZQPbZszzPJ25RWWMwyFy7EZmcXbnNVqED0+W2IadeemAvOJyQuzsGkIs5bvnwbycnT+e23nURGhlCnTvm+/E5Zo6ImEiSstSz8azcT5qUwY/VWvP7Tm43rVKTn+adwdaOahIWUzdOb3rw8spcs8Z/SnEPen3/+Y3v4WWf5V2i2JbJxY0yIfnSJWGsZO/YX7r//G3JyCmjQII7337+Zs8/WLy9liX7aiTgsJ9/DZ8s2M35+Cmu2+O5XFOIydGhckx5t6tI0sWyuTszfvNm3QnPOHDJ/+gmblVW4zRUTQ/T55/tXaLYlNL66g0lFgk9WVj69en3KtGmrALjjjqYMG3YVUVGhDieT4qaiJuKQLenZTP5pA+/9vJE9Wb7Tm1Wjw+jaMpGurZKIr1C2Tm/avDyyfllKxuzZZM6ZTe4f6/6xPfyMM4g5MNesSRNMqP7BETmc8HA3W7dmEBMTxujR13LLLY2cjiQBEvCiZoy5EhgGuIG3rLUvHLQ9EZgIVPK/ZqC19qtA5xJxgrWWJRv2MH5+Ct+s3IrHf36zYe0K9GxzCtc2rlDkjHYAACAASURBVEl4SNmZDJ+/dau/mM0hc/5PeDP/vkG9Kzqa6DZtfAsB2rYltIZuZyNyJNZaMjPziYkJw+12MWXKTWRm5nHaaVrdXJYFtKgZY9zACOAyYBOwyBjzmbV2dZGXPQm8b60daYw5G/gKqBvIXCJOmL5kExPm/8XKNN/pTbfLcM05NenZpi7NkiqXiWsc2fx8spYuJXPOHDJmzSZ37dp/bA8/7TT/Cs32RDVtggkrn7e0Ejleu3dn07Pnp2Rm5vHtt7cWXn5Dyr5Aj6i1ANZZa9cDGGOmAtcBRYuaBQ4sUakIbEaCVt/v+zInbU6x7e+qNXeRtLdBse0PoNHE4DwFMOCDXwGoHBXKLS0TubVVEjUrRjqc6uTlb9tO5lxfMcucPx9vRkbhNhMVRXTr1r6FAG0vILRWLQeTipROP/2USufOH7JxYzoVK4bz+++7tGCgHAl0UasNFLnBHpuAlge9ZjAwwxhzLxANXHqoHRlj7gLuAkhMTCz2oHJsirOkAcVe0jZUWlWs+ysuZ2eGsq9mBXq2qcv/NalFRGjpPb1pCwrI/vXXwhWauWvW/GN7WP36f6/QbNYMl0bNRE6I12t58cV5PPHETDweS4sWtZk69SZOOaVsLjCSQwuGxQRdgAnW2peNMa2BycaYhtZab9EXWWvHAGMAmjdvbh3IKUWs6L6iWPYz4qeZAPQbdfFJ7Sczt4Do533zNHpennqUV5e88BAXp1SLLrWnNwt27CBjzlzffLP58/Hu21e4zURGEt2qlW+FZtt2hNXRDZ9FTtb27ZncdtvHfPut71I1/fu3ZsiQSwgLK72/5MmJCXRRSwMSijyu43+uqNuBKwGstT8ZYyKAasD2AGeTMuqsmrrY48myHg/Zvy4nY/YsMmfPIWf16n9sDzvlFGLatSO6XVuimjfHFV5+7i8qUhLGjVvKt9/+SZUqkUyceD3XXnu605HEIYEuaouA04wxp+AraJ2BWw56zUbgEmCCMeYsIALYEeBcInKQgl27fNc0mz2HjHnz8KanF24zERFEt2xZeKumsISEI+xJRE7WgAFt2L49k4ceaq07DZRzAS1q1toCY8w9wLf4Lr0xzlq7yhjzLLDYWvsZ0B8Ya4x5EN/Cgh7WWp3aFAkw6/GQs2KF76Kzs2eTs3LlP7aHJSUR3a4dMe3aEXVec1wRZeu6biLBZMuW/Tz44LcMG3Yl8fExhIS4eOWVK5yOJUEg4HPU/NdE++qg554q8vlq4PxA5xARKNizh8y5c30rNOfOxbN3b+E2Ex5OVIsWhQsBwpKSHEwqUn7MmPEnt976ETt2ZOFyGaZMucnpSBJEgmExgYgEiPV6yVm58u9RsxUroMiAdWhCQmExi2rRAldk6b9ciEhpUVDg5amnfuD55+cCcMklp2gUTf6HippIGVOwZw+Z8+b7FgLMnYdn9+7CbSY01D9q1pbodu0Iq1u31K5EFSnNUlPT6dLlQ+bNS8XlMjz77IUMHHgBbrfL6WgSZFTUREo56/WSs3pN4QrN7OXLwfv31W1Ca9cuXAQQ3bIlrqgoB9OKyN69OZx77hh27syidu1Ypky5iXbtNNVADk1FTaQU8qSnkzlvnu+U5pw5eHbt+ntjaCjRrVoS3dY/16xePY2aiQSRSpUi6N27GUuXbmXixOupVk2/PMnhqaiJlALWWnLXrCmca5a9bNk/Rs1CatUkpm07Ytr7R82iox1MKyIHW79+D9u3Z9KqVR0AnnnmQowxuFz6JUqOTEVNJEh59u0jc/58/6jZbDw7dv69MSSEqPPO+3uF5qmnatRMJEh98MEq7rjjc6KiQvn11z5Urx6tuWhyzFTURIKEtZbc33/3j5rNInvpMvB4CreHxMcX3g0gunVr3DExDqYVkaPJySngoYe+ZeTIxQBcemk9QkNV0OT4qKiJOMiTkeEfNZtN5py5FGzb9vdGt5uo887zLwRoT/jpp2nUTKSU+P33nXTqNJ3ly7cRFubmlVcup2/f8/T/sBw3FTWREmStJfePP8icPZuM2XPI+uUXKCgo3B4SF0d0+3bEtG1HdJvWuGNjHUwrIifigw9W0bPnp2Rm5nPqqVV4//2ONG1a0+lYUkqpqIkEmC0oIGPWLDJmzSZj9mwKtm79e6PbTWTzZoULAcLPOEO/cYuUcpUqRZCVlU+XLg0ZPfpaYmPDnY4kpZiKWjnQ9/u+zEmb43SMcsmbk8Ome+8jc87f3393XDViLmjrW6HZpg3uCrrhskhpt2tXFlWr+i6zcdll9Vm06E7OPbemfvGSk6aiVg4Ud0lrW7ttse6vrPJmZZF6d1+yFi7EXbkyVbrfRnTbtkScdRbGpQnFImWBtZa33vqFBx/8ls8/78JFF50CQLNmtRxOJmWFilo5sqL7CqcjlBuejAxSe/che8kSQuLiSBw/jvBTT3U6logUo337cund+wumTl0JwDffrCssaiLFRUVNpJh50tPZeOdd5CxfTkiNGiRNGE9Y3bpOxxKRYvTLL1tITp7OunW7iY4OZeTIa+jWrbHTsaQMUlETKUYFe/aw8fbbyV29htDatUmcOIGwOnWcjiUixcRay4gRi+jffwZ5eR7OOSee99/vyBlnVHM6mpRRmigjUkwKdu5k423dyV29hrCkJJLemaySJlLG7N6dzbPPziIvz8PddzdnwYLbVdIkoDSiJlIM8rdtY2OPnuT99Rdh9euTOH4codWrOx1LRIpZ1apRvPvujezdm8PNNzdwOo6UAypqIicpPy2NDT17kb9xI+FnnEHiuLcJqVrV6VgiUgy8Xssrr/xEfr6Hxx7zrXi/7LL6DqeS8kRFTeQk5G3cyIYePSjYvIWIBg1IfPst3JUqOR1LRIrBzp1ZdO/+CV999QdutyE5uSH16lV2OpaUMypqIicod/1fbOzZk4Jt24hs0oSEsWN0yyeRMmL27A3ccsuHpKXtp3LlCCZMuF4lTRyhoiZyAnL/+IMNPXvh2bmTqObNqTNqFO6YaKdjichJ8ni8PP/8XJ5++ke8XkubNgm8995NJCZWdDqalFNa9SlynHLWrGHDbd3x7NxJdJvWvpE0lTSRMuHJJ2cyaNAPeL2Wxx67gB9/7K6SJo5SURM5DtkrVrChew88e/YQ3b4ddUaOxBUZ6XQsESkm997bkgYN4vjmm64MGXIJoaFupyNJOaeiJnKMsn75hY09euLdt4/Yyy4l4Y03cIWHOx1LRE5CQYGXt976BY/HC0CtWrEsX343V1yhW75JcNAcNZFjkLnwZ1LvvhublUWFq6+i1tChmNBQp2OJyEnYtGkfXbp8yNy5G9myZT+DBrUHwOUyDicT+ZuKmshRZMyZy6Z77sHm5lLxuuuoOeTfGLdOh4iUZl9+uZbu3T9h165sataMoW3bJKcjiRySiprIEez/4QfS7rsfm59PpZtvpsYzgzEuzRgQKa3y8jw8/vh/efnlnwC48spTmTTpeuLitCBIgpOKmshh7Pt2Bmn9+0NBAZVvvZX4Jx7HGJ0SESmtdu7M4pprpvDzz2m43YYhQy5hwIA2OtUpQU1FTeQQ0j//gs0DB4LHQ5Vevaj+8ACVNJFSrnLlCCIjQ0hMrMjUqTfRunWC05FEjkpFTeQgez/8iC1PPgnWUq3v3VS7916VNJFSKiengMzMPKpWjcLtdjF1akfCwtxUqaLL6kjpoMk2IkXsmTqNLU88AdYS98D9xN13n0qaSCm1du0uWrd+m+Tk6YWX36hRI0YlTUoVFTURv92TJrF18GAAqj/yCNX69HE2kIicsClTVtCs2RiWLdtKSspetmzJcDqSyAnRqU8RYOfYsex4+RUA4gc9SZWuXR1OJCInIisrn/vu+5q3314KQHJyA8aM6UCFCro4tZROKmpSrllr2TniTXYOHw7GUOPZZ6h8881OxxKRE7Bq1XY6dZrO6tU7iIgIYdiwK7nzznM1fUFKNRU1Kbestex49TV2jRkDLhe1nh9CxeuuczqWiJyg6dNXs3r1Ds48sxrTpnXknHPinY4kctJU1KRcstay/YUX2D1xErjd1H7pRSpcdZXTsUTkJDzxRDvCw0O4554WxMSEOR1HpFhoMYGUO9brZeuzz/pKWmgodYa9ppImUgotW7aVCy+cwLZtvoUCISEuBg68QCVNyhQVNSlXrMfDlkGD2PveVExYGAkjhhN76aVOxxKR42CtZeTIRbRq9RazZm3g2WdnOR1JJGB06lPKDVtQwOaBj7Hviy8wEREkjHyT6NatnY4lIsdh794c7rzzc6ZPXw1A797NeOmlyx1OJRI4KmpSLti8PNIGPMz+GTNwRUWRMGY0Uc2bOx1LRI7DokVpJCdP56+/9hIbG8bYsR1ITm7odCyRgFJRkzLPm5dH2v0PkPHDD7hiY0kcO4bIJk2cjiUixyEtbR9t244nN9dDs2Y1mTq1I6eeWsXpWCIBp6ImZZo3O5tN995H5ty5uCtWJOHtt4ls2MDpWCJynGrXrsCAAW3IyMhj6NBLCQ/XP19SPui/dCmzvJmZpPbtR9bChbirVCFx/DgizjjD6Vgicozmzt2I12tp1y4JgOeeu0gXr5VyR0WtPBlc8aR38cWeJ9iQG4C5XSeZLfqgx56MDFLv6k32L78QEhdH4oTxhNevf1LHEJGS4fVahg6dy6BBPxAXF83y5X2Ii4tWSZNySUVNjksgSlpS+OJi29cs25QL0tPZeOdd5CxfTkjNmiRNGE9YUlKxHUNEAmfbtgy6dfuY775bD0D37o2pVCnC4VQizlFRK08Gp5/8PvrMBKDfqItPfl+FLgYeOak9ZOYW0ODpb4m32bzXoye5a9YQWqcOiRMmEFandvHEFJGA+u9/13PrrR+zdWsG1apFMXnyDVx55alOxxJxlIqalBmVcvbz7PzR5O7bSlhSEokTJxBao4bTsUTkGLzyyk8MGDADa6F9+ySmTLmJWrVinY4l4jjdmUDKhIJt2/jP3DdJ3LeVsFPrkzh5kkqaSCnSqFF1XC7D00+357//vU0lTcRPI2pS6uWnpbG9Vw8SMnbwV6VaXD5pEiFVdH0lkWC3fv0e6tWrDMBll9Vn3br7qFu3ksOpRIKLRtSkVMvbuJGUbt0o2LSJtZXqMLh9X5U0kSCXn+/hkUe+4/TT3+CHH/4qfF4lTeR/aURNSq3c9X+xsUcPCrZvJ6xxYx6r0xHCD75Qh4gEk5SUvXTp8iELFmzC7TasWbOTiy46xelYIkFLI2pSKuWsXcuG226jYPt2os47j+ojR5MVGul0LBE5go8/XkPTpqNZsGATCQkVmDWrB337nud0LJGgphE1KXVyVq9mY6/b8ezdS3SbNtQZMZxsV6jTsUTkMHJzC3j44e94442fAejQ4XTGj7+OqlWjHE4mEvw0oialSvby5Wzo0RPP3r3EtG9PnZFv4orUSJpIMNuzJ4epU1cSGuri1Vev4NNPO6ukiRwjjahJqZH1yy+k3nkX3sxMYi+7lNovv4wJC3M6logchrUWYww1asQwbVpHYmLCOO88XYBa5HioqEmpkLlgIal9+2Kzsqhw9dXUGvoCJlSnO0WCUXZ2Pg888A21a1fgqafaA2jBgMgJUlGToJcxZy6b7rkHm5tLxeuvp+a//4Vxu52OJSKHsGbNDjp1ms7KlduJigqld+9mxMfHOB1LpNTSHDUJavtn/sCmvn2xublU6tSJmkP+rZImEqQmTlxG8+ZjWblyO6efXpV583qppImcJI2oSdDa9+0M0vr3h4ICKnfrRvzjj2GMcTqWiBwkIyOPfv2+YtKkXwHo2rURI0deQ2xsuMPJREo/FbVg9e7N8MeMYtnVVTn/ImlvA0b8NLNY9lcS0j//gs0DB4LHQ5Xbe1F9wACVNJEg9cAD3zBp0q9ERoYwYsTV9OjRRP+/ihQTFbVgVUwlDSBpb4Ni2xdAUsOqxbq/g+398EO2PDkIrKVa375Uu/ce/dAXCWLPPnsR69fvYfjwqzn77Din44iUKSpqwW5w+snvo49vJK3fqItPfl8BtmfqVLYOfgaAuAceoFqf3g4nEpGDpafnMGLEIh599Hzcbhe1asUyc2Z3p2OJlEkqahI0dk+cyLbnXwCg+qOPUrVnD2cDicj/WLx4M8nJ01m/fg8Ajz/e1uFEImWbipoEhZ1jxrLjlVcAiH9qEFVuucXhRCJSlLWW119fyMMPf0d+vpemTWtw881nOx1LpMxTURNHWWvZOXwEO0eMAGOo+dyzVOrY0elYIlLE7t3Z9Oz5KZ999jsA997bghdfvIzwcP0TIhJo+r9MHGOtZccrr7Jr7Fhwuaj1wvNU/L//czqWiBSRkrKXdu3Gk5q6j0qVInj77f/jxhvPcjqWSLmhoiaOsNay7fnn2TNpMoSEUPulF6lw5ZVOxxKRgyQkVKBevcrUqhXL1KkdqVu3ktORRMoVFTUpcdbrZeuzz7J36jRMaCi1X3uV2EsucTqWiPht356JtZb4+BjcbhcfftiJChXCCQ3VXUFESppuISUlyno8bHlykK+khYdT580RKmkiQeTHH1No0mQUXbt+hMfjBaBq1SiVNBGHqKhJibEFBWx+dCDpH32EiYwkYdRIYtpqab9IMPB4vDzzzI9ccskktmzJIC/Pw759uU7HEin3dOpTSoTNyyNtwMPsnzEDV3Q0CaNHEdW8udOxRATYvHk/Xbt+xI8/pmAMDBrUjqeeak9IiH6XF3GaipoEnDc3l7T7HyDjxx9xxcaS+NZYIhs3djqWiADffruObt0+ZseOLOLjo3n33Ru55JJ6TscSET8VNQkob3Y2m+65l8x583BXrEjCuLeJbFC89x4VkRM3b14qO3Zkceml9XjnnRuIj49xOpKIFKGiJgHjzcwk9e6+ZP38M+4qVUgcP56IM053OpZIuef1WlwuA8BTT7WnXr3K3HZb48LnRCR4BHwCgjHmSmPM78aYdcaYgYd5TSdjzGpjzCpjzJRAZ5LA8+zfz8Y77iTr558JiYsjafIklTSRIPDZZ7/TqNFItm3LACAkxEWPHk1U0kSCVECLmjHGDYwArgLOBroYY84+6DWnAY8B51trGwAPBDKTBJ4nPZ2NvW4ne+lSQmrWJOmdyYTXr+90LJFyLS/Pw4MPfsN1101l9eodjBy52OlIInIMAn3qswWwzlq7HsAYMxW4Dlhd5DV3AiOstXsArLXbA5wpcN69Gf6Y4XQKRxXs3s3G2+8gd80aQuvUIXHCBMLq1HY6lki5tn79HpKTp7N48WZCQly88MIlPPhga6djicgxCHRRqw2kFnm8CWh50GtOBzDGzAPcwGBr7TcH78gYcxdwF0BiYmJAwp604i5pp11evPsLsIIdO9jYqxe5f6wjrG5dEieMJ7RGDadjiZRrH3ywijvu+Jx9+3JJSqrItGkdadmyjtOxROQYBcNighDgNOBCoA4w2xjTyFq7t+iLrLVjgDEAzZs3tyUd8rgMTnc6QYnL37aNjd17kJeSQtip9UkaP56QuDinY4mUa7/9tpPk5OlYCzfeeBZvvdWBypUjnY4lIsch0EUtDUgo8riO/7miNgELrbX5wF/GmLX4ituiAGeTYpKflsaGHj3JT00l/MwzSRz3NiFVqjgdS6TcO/PMagwefCFVq0bSt+95GKMFAyKlTaCL2iLgNGPMKfgKWmfgloNe8wnQBRhvjKmG71To+gDnkmKSt2EDG3r2pGDzFiIaNiTxrbG4K1VyOpZIuTV58q/Url2Biy8+BfBdfkNESq+AFjVrbYEx5h7gW3zzz8ZZa1cZY54FFltrP/Nvu9wYsxrwAA9ba3cFMpcUj9z169nYoycF27cT2bQpCWNG446NdTqWSLmUmZnHPfd8zYQJy6hRI4Y1a/pRqVKE07FE5CQFfI6atfYr4KuDnnuqyOcWeMj/IaVEztq1bOzZC8+uXUS1aEHCyDdxRUc7HUukXFqxYhvJydNZs2YnkZEh/OtfF1GxYrjTsUSkGATDYgIpZbJXrSL19jvw7N1LdJs21BkxHFekJiiLlDRrLW+99Qv33fcNOTkFnH12HNOmdaRhw+pORxORYqKiJscl+9df2XjnXXj37SPmwgupPew1XOH6zV3ECfff/w1vvPEzAD17NuGNN64iOjrM4VQiUpwCfgspKTuylixhY6/b8e7bR+xll1Hn9WEqaSIO6tDhdGJjw5g8+QbGjbtOJU2kDNKImhyTzAULSL27LzY7mwrXXEOtoS9gQvSfj0hJstayaNFmWrTw3e3jssvqs2HDA7o2mkgZdkIjasYYtzGmU3GHkeCUMWcOqb37YLOzqXjDDdT6z1CVNJEStmdPNjfe+D6tWr3FzJl/FT6vkiZSth2xqBljPjTGnG6MSfY/bmCMeRUwQL+SCCjO2j9zJpv69sPm5lIpOZma//4Xxu12OpZIubJgwSaaNh3NJ5/8RmxsOBkZeU5HEpEScrRhkTj/awYZYzYCQ4EB/uujeQOeThy175tvSRswAAoKqHxbN+Ife0xXNhcpQV6v5eWX5/P44zMpKPBy3nm1mDq1I/XqVXY6moiUkKMVtQJr7WpjzPVABWAg0NEYszjw0cRJ6Z9/zuZHB4LXS9U7bieuf3+VNJEStHNnFrfd9jFff70OgIceasXzz19KWJhGtEXKk6NONDLGRADPAb2AH/Hd3skAwX1jdDlhez/8kC1PDgJrqdavH9Xu6aeSJlLC8vM9LFmyhSpVIpkw4To6dDjD6Ugi4oDDFjVjTFfgDHy3dZoPTAOuAyYCvYHaxpi+QKi1dlgJZJUSsOe999j6zLMAxD34INV63xWwY1lr+XLFFnZnnvx8m7wCnYmX0s/j8f137Ha7qFkzlk8+SaZOnQokJFR0OJmIOOVII2r1gWzgO+BxoCvwNL5ToDWBcP+foQHOKCVk14QJbH9hKADVBz5K1R49Anq8935O5fGPVxTrPiNCdVpISqetWzO49daPaNcuqfBG6q1bJzicSkScdtiiZq191hhzEXAX8BoQBiwA2lhrBxljLrDWDiqhnBJgO0ePYcerrwIQ/9QgqtxyS0CPl56Vz0szfgegQ+NaVIosnr7f/vS4YtmPSEn6/vv1dO36Edu3Z7Jq1Q4efLAVsbG6mLSIHH2OmrXW/mGMuQdoC/wGvBj4WFJSrLXsfGM4O998E4yh5r+eo9JNNwX8uMP++we7M/NoeUoVXu/cRHPgpFwqKPAyePCPDBkyB2vhoovq8u67N6qkiUihoxU1lzGmOvAhvnlpnYFV/m1aTFDKWWvZ8cor7Br7Frhc1Br6AhU7dAj4cddt38+kn1JwGXi6QwOVNCmXNm3aR5cuHzJ37kZcLsPgwe154om2uN26s5+I/O1oRW0dEAW8Yq1dZIz5DZhtjPkIiAh4OgkYay3bhjzPnsmTISSE2i+9SIUrryyR4z7z+WoKvJauLRM5u1aFgB9TJBjdf/83zJ27kZo1Y5gy5SYuvLCu05FEJAgdsahZa+/wf5rif7zfGNMM3+U5XghsNAkU6/Wy9Zln2TttGiY0lNrDXiP24otL5Nj/XbOdOX/spEJECP0v1+UGpPx6442riIwM4dVXryAuLtrpOCISpI57jN1a67XWeqy1nxmfpoEIJoFhPR62PPGkr6SFh1PnzRElVtJyCzw89+VqAB687HSqRIeVyHFFgsFff+3hwQe/KbwER61asbzzzo0qaSJyREctasaYMP+pzgOPJxTZHAKMCkAuCQCbn8/mRx4l/eOPMZGRJIweRUzbtiV2/HFzU9iwK4vTqsdwa6ukEjuuiNM+/HA1TZuO5rXXFjJs2EKn44hIKXLUOxNYa/OMMUUv5tOoyLZ8Y4zuDlxKpD3Un/3ffYcrOpqEMaOJatasxI69fV8Ow2f+AcBTHc4mVBOmpRzIySlgwIAZjBixCIDrrz+THj2aOJxKREqTI92Z4BEgD9/qzjhjzH345qZV9X9+gFZ/lhL7v/sOV4UKJI4dQ2TjxiV67KHf/E5mnofLzo6n7Wm61pmUfWvX7iI5eTrLlm0lLMzNSy9dxj33tNAqZxE5LkcaUQvDV8K8+E6RHrgiadHPJch5s7MLP3dXqkTC228R2aBBiWZYunEPH/6yiTC3iyevOatEjy3ihF9/3coFF4wnIyOP+vUrM21aR5o1q+V0LBEphY50Z4J/GWOuBrYDydbalwGMMbce+Nz/+LrAx5QT4c3MJPXuvhDVHYDEiROJOOP0ks3gtQz+3LeA4Pa2p5BUVROnpexr2LA6zZrVpEaNGMaM6UCFCrqArYicmKPNUWsNNAcSjTGfAa8CXx/YaIxx4xt5kyDj2b+f1Lt6k710KVzoK2olXdIAPl6axq+pe6keG06/i04t8eOLlJRVq7ZTtWoUNWrE4Ha7+PLLW4iKCtWpThE5KUec0e2/l2dn4HlgJ1DBWvt4kZcYYFzg4smJ8Ozdy8aevcheupSQWjUdy5GRW8AL3/wGwMCrziQm/KhrV0RKHWst48Yt5bzzxtK160eFl9+Ijg5TSRORk3bIfzn9I2X1gVzgGuAMYCyQZoxJLPJSNzAr0CHl2BXs3s3GXreT+9tvhCYkkDRhPDz3uyNZhs9cx479uTRJqMT1TWo7kkEkkPbvz6VPny+ZMmUFAAkJFcjP9+o2UCJSbA43xFEReBfI8T+2wFD/50V/RQzxf5z3/+zdeVxU1f/H8dcddkEF3BAURXHhpyIqivsaluYuKqKVpqmpaWWL5ZJlX80y8+tWpt/cd80lNTWhKNfEJdwVdxTc2Ydt5vz+ICdREVRoAD/Px4NH3DtnznwGCd6cc+85eVKdeCLpN29yqX9/UiPOYV2xIu6LFmJVpgzw7we1i7cS+WHXBQAmdKqBTicjC6JwOXw4il691nL27B3s7a2YM+dlXn31372bWghR+D0yqCml7vB3+NI0zQb4CTgJ/KGUWvvvlff8+mLCMopGP8W0Zbl3odzfn39yHDiePng1TwAAIABJREFUm2Xl2OdbTpJqMBJQrxw+5R3NUoMQeeXbbw/w9tvbSU01UKtWaVav7kH16iXNXZYQohDKyYK3KZqmDQSqA6XzviQBPF1Ie4x4l6hc7e9xfj9zk50nr2NvbcEHL8p+nqLwiYpKIDXVwODB9fjmmxexs5MVi4QQeSNHV3crpS4Dl/O4FvEIw757/D6cqZcucalff9KjorCtVQv3ed9j4Wi+Eaw0g5HPNmcsx/FWmyqULmZrtlqEyE16fZopkI0f34KmTd1p27aymasSQhR2j73iVdO0K5qmXdA07XwWHxc0TVv/bxUrMks5f55LfV8hPSoKuzp1cP/hf2YNaQBL9l4i4kYCFUsUoX+TimatRYjcoJTim2/2Uq3aLK5fTwDA0lInIU0I8a/I7takaMATqAJUBW7//fm940ige14WKB4t+fQZLr3yKuk3b1KkQQPc58/DomhRs9Z0OyGFb3aeAWDsy/+HjaWFWesR4lndvp1Ep04reffdHVy5EsfGjea5g1oI8fzKbupTKaUM9w40TXvUsTHPqhOPpD9+nCuvD8AQG4t9kyaUmzUTnZ2ducvi61/OEJ+cTvOqpWjjJZczioJt167L9O69jsjIOBwdbVmwoDNdulQ3d1lCiOeMrEBawOj/+ovLA9/AGB+PQ6tWuE3/Bp2N+benOX4tlhV/XsZSpzG+g5cs9CkKLKNRMWXKLsaN+xWDQdGoUTlWrOhOhQpy97IQ4t+XXVCroGnavZ0HNMDjgeOqmqZNVkp9lGcVCpOksDCuDBqMMSmJom3b4jb1KzRr8+/gpZTi000nUApebVwRz9LmnYIV4lkcPHiNMWNCUAo+/LAJEye2wspKpvGFEOaRXVDrD6SSseAtwNL7PtfIWBQ3PW9KE/dL3LuXK0OHofR6inXogOsXk9Es88eA6ObwKP68eAdne2tGvlDF3OUI8Uzq13djypQXqFWrDC+9JPvTCiHMK7vf9J8DK4ENSqkzmqa1Bz4gY0upX5RSr+Z1gQISfv+dyOFvoVJTKd61K2U/n4hmkT/+wtenGpi89SQA77WtRnFZT0oUMOnpRj77LJQWLSrQpk0lAN5/v4mZqxJCiAzZBTUdEAfM1jTNi4zdCYYB54FwTdO8lVLheVzjcy0+OJirb7+DSkvDMbAXLuPHo+nyzz6C34We41psMjVci9GrfnlzlyPEE7l6NY6goB/5/fdL/O9/RTl3bgS2tvljpFoIISD7oBYHfE/GhuzOwB3+mfocCFzSNE0nd37mnciRb0N6Ok6vvkKZjz7KVxfpR95N4rvQcwB80rEGFrKfpyhAtm49y2uvbeDWrSRcXBxYsqSrhDQhRL6T3U+lskA48GAQ08gIbBaANRnrqom8kJ5OiTcGUurdd/NVSAOY/PMpUtKNdPAuSwMPZ3OXI0SOpKUZGDMmhK++2gNA27aVWbKkK6VL25u5MiGEeNhjg5pS6pEBTNM0HdBFKfVjnlT1nItZu5aMAUwoOXw4JYcNzXchbd/522wJj8LWSsdH7b3MXY4QOdaz51o2bDiFhYXG55+35oMPmqCT0WAhRD6V3RZSqzRNyyrMjdU0Tadp2lwtv6WIAuzO8uVEjR1nOi41fFi+C2kGo+LTnzL28xzSojJujuZfbFeInBo2rD4VKhQnNLQfo0c3lZAmhMjXspv6rKuUStc07X0g5YHHDEA3wE0ppR5+qnhStxcs5MaUKeYuI1srD1zmZFQcbo52DG4u+x2K/C0lJZ2QkAu0a5cxQfDCC5U4fXo4NjZyPZoQIv/Ldgupv/87HLgEVAIO8U9o6wOMe8TzxBO69d1cbk6fDoDLJ+Mh1MwFZSE2KY2p2zP2O/y4vRd21vljmRAhHiUi4g69eq3l8OEodu58ldatPQAkpAkhCoyc/rS6DOwG7IDNQCzwHnBIKXU4j2orcIaWKcUfRexgUa2cP0kpev5hJGC3wgjMba/j19RJDOG/eVbn04pJSmXwkoPcTUrDz8OZ9rVczF2SEFlaufIYgwb9RHx8Kh4ejhQrZv6t1oQQ4kk9Mqj9fc3Z50AxTdOa/31a8c8IG2Rs2D4xj+srUP4o8oTXailFn9+MdN6nMGgwq6OO3TXyzxpp97t0O5H+Cw9w/mYipYvaMKlbrXx37ZwQAHp9GiNHbmPevEMA9Ojxf8yb15HixW3NXJkQQjy5rEbUdEDi348PIyOgPfRbWdM0S6WUbCH1gKOvHc22jVKK65Mmc3ffErC0xH3qVL576UXT47P3huRliU/k4KU7vLH4IHcSU6nuUpQf+tXHVW4gEPlQRMQdunZdxbFjN7CxseC//32JQYPqyR8VQogC65HDN0opg1JqEnBHKdWLf9ZNMzX5+9wmTdOK5H2ZhYsyGon+ZAJ3lyxBs7Ki3IwZFLsvpOUnm8Ov0Xvefu4kptKiainWvtlYQprIt+ztrbhxI5GqVUuwf/9ABg/2lZAmhCjQcnqNmifgALgBfYE0MsJaKBk7FMzIk+oKIWUwEDVmLLEbNqDZ2FBu1iwcmjU1d1kPUUrxbeg5vtyWceNAHz93Pu1UA0uL/Dk1K55fiYmp2NpaYmGho2zZomzf3hdPT2ccHKzNXZoQQjyznAa194DkB859CMwGdiBBLUdUWhrXPhxN3NataHZ2lP/2W+wb+pm7rIekGYyM23CMlQeuoGnwcTsvBjbzkJEJke+Eh1+nZ881BAXVYvz4FgD4+MhNLkKIwiO7oHZO0zQrpdSy+09qmmYBjFVKJWiatlz2+8yeSk3l6qhRxP+yE529PeW/n0uRevXMXdZD4pLTGLr0ELsibmFrpWN6Lx9eqlnW3GUJkYlSiu+/P8jIkdtISTGwbt1JRo9uirUsFyOEKGSy20KqXVYPAR/93WZWbhdV2BhTUrg6YiQJoaHoihXDff487Ly9zV3WQyLvJvH6wgOcuZ5ASQdr5r9WH5/yjuYuS4hMYmOTGTRoM6tXHwdg4MA6/Pe/7SSkCSEKpada9fHv0bNtuVxLoWTU64kcNpzEPXuwcHTE/Yf/Yft//2fush4SHhnDgEVh3IxPwbO0Awv61ae8s9wnIvKXsLBr9Oq1lvPn7+LgYM3cuR0ICnqCdQuFEKKAkeW585AhIZHIN98k6cABLEqUwH3BD9hWrWrush6y/Xg0I1ceJjnNSOPKJfi2bz2K21mZuywhHjJ2bAjnz9+lTh0XVq0KoEqVEuYuSQgh8pQEtTxiiI/nyhuD0B85gmXp0rgvXIBNpUrmLisTpRT/23WB/2w9iVLQo145/tO1FtaWcmenyJ9++KEzM2bsZ8KEltjayo8vIUThJ7+R84AhJobL/V/PCGmuZamwdEm+C2npBiOfbDrO51syQtr7L1bjywBvCWkiX9mz5wqvvbYBgyHjXiVX16J88cULEtKEEM8N+WmXy4omKS7160/KqVNYlS9PhYULsHJzM3dZmSSkpPPW8kP8evom1hY6vurhTWef/FWjeL4ZjYqvvtrNmDEhGAyKpk3L88Yb+e8uaSGEyGsS1HKRY4Ji3AoDKbdOYV2xIu6LFmJVpoy5y8okOjaZ1xce4ERUHE5FrPj+VV/qV3Q2d1lCmNy4kcirr65n+/ZzALz/fmP69fMxc1VCCGEeEtRySVp0NBOWGnC9CzZVPHH/4QcsS5Uyd1mZnLgWx+sLDxAdl4xHSXsW9KtPxZL25i5LCJPffrtIUNA6oqISKFHCjsWLu9K+fRVzlyWEEGYjQS0XpEZe5XK/frjehQtloO3ixVg6OZm7rEx+PXWD4csPkZhqoEFFZ+a+Ug8ne9liR+Qfv/12kTZtFmM0Kpo1c2f58u6UK1fM3GUJIYRZaUqp7FvlM76+viosLCx3OlvWA87ueOqnp8ZbcOnXEqQnWbLH902SHWrmTl1/+7OBwzP3YVSK3RG3MCro7OPKlwHe2FjK4qAifzEYjLRtu5QmTcozfnwLLOXGFiFEIaFp2kGllO/TPFdG1J4hpKXEWnL51xKkJ1tgVzIl10PaOUsDoWdu5lp/I9pU4Z0XqsienSLf2LHjHN7eZXBxccDCQsf27X0loAkhxH0kqN0zIfaJmiefPs3l/q9jSL5DET8/ys+ZDaP2AzDsu9ZPVcKVO0n0X3iAiBsJlHSwYXzHGnTMpWUIyha3pbqLTCOJ/CEtzcC4cb8yZcpuWrf2YMeOvlhY6CSkCSHEAySoPQX9seNcGTAAQ2ws9k2bUm7WTHS2ts/U5+HLd3ljcRi3ElKpWsaBH/rVp5yTbOEkCp/Ll2MJDFzL3r2R6HQabdp4yCivEEJkQYLaE9IfOcLlNwZhjI/HoVUr3KZ/g87G5pn63HYsipErj5CSbqRZlZLM7lOXYrayhZMofDZuPEX//hu5ezcZN7eirFjRnWbNKpi7LCGEyLckqD2BpLAwrgwajDEpiaJt2+I29Ss066e/c1Ipxbw/zjP551MoBYH1yzOxS02sLGT6RxQuSinee28H06btA+Dll6uwcGEXSpaUUWMhhHgcCWo5lLh3L1eGDkPp9RTr0AHXLyajWT79ly/dYGT8puMs338ZgA9fqs6QFpVkCkgUSpqm4eBgjaWljilTXuCddxrK97oQQuSABLUcSAgNJfKtEajUVIp360bZiZ+hWTz98hbxyWkMX36Y0DM3sbbU8U1PH172LpuLFQuRP9y6lWQaNRs/vgXdu/8f3t75a7cOIYTIz2SOLRvxwcFcGf4WKjUVx96BlP184jOFtGsxenp8t5fQMzdxtrdmxRsNJaSJQkevT2PIkM3UrDmH69cTALCw0ElIE0KIJyQjao8R9/PPXH3/A0hPx/m1Vyk9evQzTdccuxrL6wsPcCM+hUqlMrZwqlBCtnAShcupU7fo1Wst4eHXsba2YP/+q3TqVM3cZQkhRIEkQS0LsRs3cu2jj8FopMQbb1Dq3XeeKaTtPHGdESsPk5RqwM8jYwsnxyKyhZMoXJYs+Ys339xCYmIanp7OrF4dQJ06MmIshBBPS4LaI9xds4bo8Z+AUpR8azglhw59ppC2cPcFPtt8AqOCbnXc+KK7N9aysKcoRBITUxk+/GcWLjwCQO/eNZk7twNFiz7b0jVCCPG8k6D2gDvLlnF94ucAlBr1LiXfeOOp+zIYFRM3n2DhnosAvPNCVUa08ZS73UShc/BgFIsWHcHOzpKZM9vx+ut15PtcCCFygQS1+9z+YQE3vvwSgDIff4Tzq68+dV+JKemMXHmYnSdvYG2hY0pALbrWKZdbpQqRrzRvXoFZs9rTvHkFatYsbe5yhBCi0JD5t7/d+u47U0hzmfDJM4W063HJ9Pp+LztP3sCxiBVLBjSQkCYKlbi4FPr0+ZGQkAumc0OH1peQJoQQuSzPg5qmaS9pmnZa07QITdNGP6Zdd03TlKZpvnld0/2UghvhRbk5/b+gaZT9z39wCgx8pj67zt7NsatxVChRhB/fbIxfpRK5VK0Q5nfoUBR1685l+fKjDB68GYPBaO6ShBCi0MrToKZpmgUwG2gH/B/QW9O0/3tEu6LASGB/XtbzIKUUN/4qxu0TRcHCAtcvv8Sxe7dn7vdabDL1KjixfmgTKpVyyIVKhTA/pRQzZ+6nUaP/ce7cXWrXLsOWLUFYyJZnQgiRZ/L6J2wDIEIpdV4plQqsBDo/ot1EYAqQnMf1ZBL/88/cOeUAmsJt2jSKd+zw1H1FxepNn3fwLsuygX4428vyG6JwuHtXT7duqxkxYhupqQaGDvVl376BVK0qo8VCCJGX8jqouQFX7juO/PuciaZpdYHySqktj+tI07RBmqaFaZoWdvPmzVwpLuV8xvU1ztUSKfZi22fq68KtRNPnMwLrYGv19LsXCJGfKKVo124ZGzacolgxG9as6cHs2S9jayv3IgkhRF4z65yFpmk6YBowKru2SqnvlVK+SinfUqVK5WodOguVu/3pZFkCUXhomsbnn7fGz8+Nw4cHExDw0NULQggh8kheB7WrQPn7jsv9fe6eokBN4DdN0y4CDYFN//YNBUKIzG7eTGTFiqOm4xdeqMSePQOoVMnJjFUJIcTzJ6/nLg4AVTRN8yAjoAUCQfceVErFAiXvHWua9hvwnlIqLI/rEkJkITT0IkFBPxIVFY+LiwOtWnkAMlIshBDmkKcjakqpdGA4sB04CaxWSh3XNO0zTdM65eVrCyGejMFgZOLEUFq3Xsy1a/E0aeKOp6ezucsSQojnWp5fDayU2gpsfeDc+CzatszreoQQD4uKiqdv3/WEhFxA02DMmGZMmNASS9mTVgghzEpu2xLiObd/fySdOq3kxo1ESpe2Z+nSrvj7VzZ3WUIIIZCgJsRzz929OACtW3uwbFk3XFxkkWYhhMgvJKgJ8RyKioqndGl7LCx0lC1blN27X8fDw1F2GRBCiHxGfioL8ZzZvPkMNWt+y3/+84fpnKens4Q0IYTIh+QnsxDPidRUA6NGbadjxxXcuaPnwIFrGI25u9izEEKI3CVTn0I8By5cuEuvXms5cOAalpY6Jk1qzahRjWVtNCGEyOckqAlRyK1bd4IBAzYRG5uCu3txVq7sTqNG5bN/ohBCCLOToCZEIaaUYvbsA8TGptClS3X+979OODvbmbssIYQQOSRBTYhCTNM0li7txsaNpxgyxBdNk6lOIYQoSORmAiEKmeXLj9Kly0oMBiMArq5FefPN+hLShBCiAJKgJkQhkZSUxsCBm+jT50c2bjzNhg2nzF2SEEKIZyRTn0IUAseP36Bnz7WcOHETW1tLZsx4iW7dvMxdlhBCiGckQU2IAkwpxYIFRxg+fCt6fTrVq5dk9eoAatUqY+7ShBBC5AIJakIUYOvWnWTAgE0A9Ovnw6xZ7bC3tzZzVUIIIXKLBDUhCrCuXavToUNVevT4P159tba5yxFCCJHLJKgJUYAopZg37xCdOlXDxcUBCwsdmzYFyh2dQghRSMldn0IUEDExyQQErGHw4M307fsjSmXs0ykhTQghCi8ZUROiANi/P5LAwHVcvBhDsWI2DBpUTwKaEEI8BySoCZGPGY2Kb77Zy+jRwaSnG6lXryyrVgVQubKzuUsTQgjxL5CgJkQ+ZTQqunZdxaZNpwF4+20/vvjiBWxs5H9bIYR4Xsg1akLkUzqdRu3aZXBysmXjxkC++eYlCWlCCPGckaAmRD5iMBg5d+6O6Xj8+BYcPfomnTpVM2NVQgghzEWCmhD5RHR0Ai+9tIwmTX7g+vUEACwtdbi5FTNzZUIIIcxFgpoQ+cDOnefx8fmOnTvPYzQqLlyIMXdJQggh8gEJakKYUXq6kXHjQmjbdgnXryfSsmVFjhwZQsOG5cxdmhBCiHxArkwWwkwiI+MIClrHH39cRtNgwoQWjB3bHAsL+ftJCCFEhgIZ1I7fPk6tRbWeuZ+AI0aq1XqT2yVqwpCQXKhMiJw7ffoWu3ZdpmxZB5Yt60arVh7mLkkIIUQ+UyCDWm66XaJmrvZ3tfjlXO1PFC5KKdOOAm3aVGLx4q60bVuZ0qXtzVyZEEKI/KjABrWjrx195j5uxs9m9bGMz4d91/qZ+tpz7hZB8/bTsJIsoyAe7eLFGPr0+ZGJE1vRunXG6Fnfvt5mrkoIIUR+JhfDCPEvWL/+JHXqzGXPnit8/HGwaUN1IYQQ4nEkqAmRh1JS0hkx4me6dVtNTEwynTpVY+vWPrKhuhBCiBwpsFOfQuR3ERF36NVrLYcORWFlpeOrr/wZMcJPQpoQQogck6AmRB4wGIy0a7eMiIg7VKrkxKpVAfj6upq7LCGEEAWMTH0KkQcsLHTMmdOeXr1qcOjQIAlpQgghnooENSFyyYkTN/n++4OmY3//yqxcGUDx4rZmrEoIIURBJlOfQjwjpRSLFv3FsGFb0evT8PIqSbNmFcxdlhBCiEJAgpoQzyAhIZWhQ7ewZEk4AK+84k2dOmXNXJUQQojCQoKaEE/pr7+i6dlzLWfO3KZIESvmzGnPa6/5mLssIYQQhYgENSGewk8/naZHjzWkpBioWbM0q1cH4OVVytxlCSGEKGQkqAnxFHx9XSle3JYuXaoxffpL2NlZmbskIYQQhZAENSFy6NixG3h5lcTCQkfZskU5evRN2UxdCCFEnpLlOYTIhlKKb77ZS926c/nPf/4wnZeQJoQQIq/JiJoQj3H7dhL9+2/kp5/OABATk4xSSraBEkII8a+QoCZEFnbvvkxg4DoiI+NwdLRlwYLOdOlS3dxlCSGEeI5IUBPiAUajYsqUXYwb9ysGg6Jhw3KsXNmdChUczV2aEEKI54xcoybEA5RS7NhxHoNB8f77jfn9934S0oQQQpiFjKgJ8TeDwYiFhQ4LCx3LlnXjr7+iadeuirnLEkII8RyTETXx3DMYjEyY8Bvt2i3DYDAC4OpaVEKaEEIIs5MRNfFcu3Ytnj59fuS33y6iabBr12VatKho7rKEEEIIQIKaeI5t2xbBK6+s59atJFxcHFi6tKuENCGEEPmKBDXx3ElLMzBu3K9MmbIbAH//SixZ0pUyZRzMXJkQQgiRmVyjJp47CxYcYcqU3VhYaEya1Jpt2/pKSBNCCJEvyYiaeO4MGFCH0NBLvPmmL02bupu7HCGEECJLMqImCr2UlHTGjAkmOjoBwLT8hoQ0IYQQ+Z2MqIlC7dy5O/TqtZaDB6M4fDiarVv7mLskIYQQIsckqIlCa/Xq4wwcuIn4+FQ8PByZMKGluUsSQgghnogENVHo6PVpvPPOdubOPQhAQMD/MW9eRxwdbc1cmRBCCPFkJKiJQiU11UDjxj9w5Eg0NjYWfPPNiwwZ4oumaeYuTQghhHhiEtREoWJtbUHHjlVJTExl9eoe+Pi4mLskIYQQ4qkVyKBWKsGd2UNCcqEnr1zoQ5hbQkIq58/fxdu7DADjx7fg/fcbU7SojZkrE0IIIZ6NLM8BlHWIN3cJ4imFh1+nfv15tG27hOvXM5bfsLTUSUgTQghRKBTIETWAYd+1fuY+bs6aza1Zsyg5dGguVCT+TUop5s07xMiR20hOTqdGjVLExaXIDgNCCCEKlQIb1MTzKy4uhUGDfmLVquMADBxYh//+tx1FiliZuTIhhBAid0lQEwXKoUNR9Oy5hnPn7uLgYM3cuR0ICqpl7rKEEEKIPCFBTRQod+7oOX/+Lj4+LqxaFUDVqiXMXZIQIhtGo5Fbt24RExODwWAwdzlC5AlbW1vKlSuHlVXuzu5IUBP5XmqqAWtrCwBeeKESmzb15oUXKmFrK9++QhQEkZGRaJpGxYoVsbKyknUNRaGjlOL27dtERkbi4eGRq33LXZ8iX9u79wrVq88iJOSC6VyHDlUlpAlRgCQmJuLm5oa1tbWENFEoaZpGiRIlSE5OzvW+JaiJfMloVHz55W6aNVvAhQsxzJix39wlCSGegU4nv25E4ZZXf4TIsITId27eTOTVVzewbVsEAKNGNWLSpDZmrkoIIQo2pZSMaP6tIH0t5E8cka+Ehl7Ex2cu27ZF4Oxsx08/9Wbq1Lama9SEEOLf9Pbbb3Pnzp2Hzg8ePJhZs2Y9VZ+fffYZ33///WPbdOzYka1bt2Y616pVK3bu3PlUr7lq1Spmzpz5VM8tjLp168bNmzfNXUaOSFAT+UZycjq9e6/j2rV4mjZ156+/htChQ1VzlyWEKMR2795NmTJlaNq0KY6OjkRERBAfH098fDwhISEcPHgQZ2dnDAZDpjtWbWxssLe3B+Dq1auUKFECHx8fPDw8GD9+fKbXSEtLIz093XRsZWWV6c5Ag8FAWlpapucYjcaHpouLFSuGpeWTT4RdvnyZuXPnMmTIENO5MWPG0L17d9PxwoULCQwMNB2PHj2aCRMmAHD48GFq1KiBq6srY8aMeeLXz86GDRsoX748Xl5eHDp0KNv2gwcPxtXVFQ8PDzZt2mQ6P378eEqXLk2bNm1M4To6OhpbW1tcXFxwcXGhZ8+eAHz88ccMHDgw199LXpCpT5Fv2NpasmhRF3777SKfftoKS0v5O0IIkbesra3x9/dn6NChzJ49m6+//pq6dety4MABli9fTtWqVWnatCnp6emMHj2aDh06oJRCp9OhaRrJyclYWlpSoUIFDh06xPTp0x8agZs/fz6LFy9Gp9Nx8uRJ9Ho9tra2jB49mipVqmAwGAgKCuKtt97CYDBgNBrRNA2lFCkpKVhYWGBhYZFpqi42NpaiRYvm6Nq/8ePHM3XqVKytrU3ngoODOXv27CMD4f3S09MJCAhg2rRptGvXjubNm9OiRQvatm37FF/th924cYOBAwcSGhqKnZ0dPXv2JCwsLMv2W7ZsQa/Xc+3aNcLDw2nTpg1RUVFs376d9evXExERwZYtWxg7dixz5szhyJEjvPbaa8ydOzdTP/Xr16dcuXL8+uuvtGrVKlfeS16RoCbMaseOcxw/foN33mkEgL9/Zfz9K5u5KiFEXqo4esu/8joXv3g52zb3ws+WLVvo3LkzDg4OzJ07l3HjxnHq1ClCQ0MzBaSQkBCGDx9OZGQkzs7OTJs2jQ0bNmQKOw8GnzfffJOAgACmTp0KQMuWLSlbtiwbN26kbt269OjRgxo1agCwa9cu3nrrLc6ePcupU6eoVKkSHTp0YNGiRRw5coRDhw5RunRpUlNT+eWXXyhTpsxj319KSgqXLl2ibt26pnNxcXFERkZSo0YNjhw5kumxB+3atQs7Ozs6d+4MQJcuXQgODs4yqA0fPpy1a9dmOufv78+SJUse2X779u34+/ub3r+7uztnzpyhatVHz6acOHGCRo0yfl94e3ujaRqJiYn8+OOPDB8+nGLFihEYGMikSZOAjNFAHx+fR/Y1aNAg5syZk++DmgxZCLNITzfy8cfBvPjiUkaN2sGBA1fNXZIQ4jm2fv16/P39admyJUuXLmXgwIEcPXqU3r170717d6pXrw5A69atOXHiBA4ODkyYMIHw8HAcHBweOyX5zjvv4O7uTnp6Ojt37qRIkSL2YHtQAAAgAElEQVQUKVKEbdu2UadOHT7//HNGjhwJQIsWLdizZw9KKWbNmsX27dsZMWIEixYtwmg00qhRI8LCwggPD882pAGcP38eLy+vTOdCQ0Np0qQJLVu2JDg4+LHPP3bsGNWqVTMd9+/fn8GDB2fZftasWURHR2f6yCqkQcYae97e3qbjChUqEBERkWX7mjVrsmLFCu7evcvq1aupWLEixYsXz9SPpmlYWVmRmJjI4cOH+eqrryhTpgwNGzbkzJkzpr5q166d6Ti/yvMRNU3TXgL+C1gA85VSXzzw+LvAQCAduAm8rpS6lNd1CfO5ciWW3r3XsXv3FXQ6jU8/bUndumXNXZYQ4l+Sk5Guf9tLL73EsmXLWLlyJQaDgdDQULy9venbty+JiYlMnDjR1HbPnj1ERUWxatUqSpUqRe3atbG1tc2y7yFDhjBq1Ch69+6NnZ0dFSpUwNnZmXXr1nHx4kU2bNiQqf26detISUnh448/5vz58wwdOpSZM2fi6OjIzp07OXr0KLVq5WzrvLt37+Lk5JTpXHBwMC1atKBatWpMnTqV999/P8vnx8TE4ODgYDrOSTh8EgaDIVN99vb2xMTEZNm+devWfPDBB7Rv356rV68ye/ZsUz/FihUztStSpAixsbFUqlSJ0aNHU7duXaZOncq7777L5s2bTe2UUrn6fvJCngY1TdMsgNmAPxAJHNA0bZNS6sR9zQ4DvkqpJE3T3gS+BHrlZV33GP/+B4q4mcDBY9HP1Nfp6PjcKKnQ++mn0/Trt5E7d/S4uhZlxYruNG9ewdxlCSGec5988gne3t7s3buXoKAg7O3tsbCwwNHR8aHRsunTp1O9enXq16/PiBEjWL58+UNh6J7w8HD69+9PcnIyZ8+epXHjxnzwwQd89tlnnDp1irS0NDZu3EhaWhrbtm2jWLFiTJ06lXr16tGnTx82b95Ms2bN2Lt3L82bN6devXoEBQWxZ88eihYtmu37KlGiBLdv3850Ljg4mGXLlqHT6UhKSnroRob7WVlZkZKSYjoODQ3lypUr9O3b95Htn3Tq08nJKVMw0+v1j71mbtKkSQQFBfHRRx+h1+tp0qQJdevWzbKfL774Z2xo6NChmW70KChLdOT1iFoDIEIpdR5A07SVQGfAFNSUUr/e134f8Oh//VwWk5TKhgMXaQ5sDo9iWdrBXOnXykJmk7Myb95BBg3K+EumXTtPFi3qQqlS9mauSggh4OTJk8TFxaHX603nrl+/ztixY0lNTTWd27NnD+Hh4bRt25ZKlSqxZ88e9u3bh7u7+yP79fb25uDBg7Rs2ZKrV6/yyiuv4Ovry/fff0/Dhg1xdHTklVdeYejQoQAsXbqUOnXqcOfOHby8vBg8eDDNmzfn448/ZuXKlTRv3hy9Xk/Tpk1ZuXLlQ9OaD6pUqRInT540Hd+4cYPo6GjT0hSNGzdm37592NraZnqfqampODk54enpybp160zn//jjD+Li4rJ8vVmzZj3RsiW+vr6ZRivDwsLo2rVrlu337dtn+lrZ2dlRuXJlwsPD8fX1Ze/evTRp0oSkpCQuXLiAk5MTixcv5tVXXwUy7s61sLAwBbQDBw6Yro3Lz/I6qLkBV+47jgT8HtN+APDzox7QNG0QMAigfMlnW7Lh0u1E+i84QL+LGfPgTl5Vaft/zz6ca6HTeKWRjA5lpUOHqri5FeXttxvy7ruN0Ony/18yQojnw5YtW1iwYIFpyY3U1FQ8PDz47bff+OSTT/j554xfTWlpaUybNo0dO3YAGVOBISEhNG7cOMu+ly5dStGiRfH09MTLy4t58+Zx6NAh+vfvT8mSJZk/fz5NmjShdu3a9OjRg1atWpmuA5sxYwaOjo706tWLlStXAhmjSmlpaY8dCbvHysoKLy8vdu3aRdOmTQkJCaF+/fqmx/38/AgJCaFnz5688847REdHY29vzy+//MLXX39Nw4YNGTJkCMHBwfj5+bFmzZpMo1TPqkGDBpw9e5b58+djNBqJiIigQYMGWbb38PBgwYIFVKlShXPnzrFz506+/PJLqlatSsuWLfH29mb9+vW0bt0aGxsbJk2ahLu7Oz4+PkycOJFOnTqZRtG+/fbbx15vl28opfLsAwgg47q0e8evALOyaNuXjBE1m+z6LV+yqnpaYRdvqzqf7VAeH2xSB2v5qBPVqqvUqKin7k883s6d51R6usF0nJiYasZqhBDmcOLECXOXkKW9e/eqPn36mI5/+OEH5ejoqJYsWaJCQ0OVUhn137p1S6WlpZnaDRs2TM2fP19dunRJeXh4qKSkJKWUUtOnT1djx45Vc+bMUUuXLlUXLlxQVapUUVFRUapOnTrKaDSqUaNGqb59+6rVq1erSZMmqfDwcOXh4aGOHj1q6r9du3Zqy5YtSimlEhISlFJKde7cWYWEhDzxe4yOjlZNmzZVcXFxauDAgWrChAmmx1asWKGaNWumlFJq8uTJys3NTZUtW1a9/fbbpjZ//vmnql27tipbtqwaN27cE79+dk6ePKn8/f2Vn5+f2rt3r1JKqcuXL6tatWo91Pbq1avK399f2dvbK2dnZ/Xtt9+aHtu0aZOqV6+e6tSpk4r6+/f67t27laenp3J2dlZBQUHq9u3bSimlduzYoYKCgnL9vWT1vQ6EqafNUk/7xBx1Do2A7fcdfwR89Ih2LwAngdI56fdpg9qmI1dVlTFbVYUPN6tRk9eoE9Wqq7OtWj9VX+Lx9Po09eabmxVMUBMm/GrucoQQZpSfg9rvv/9uCmqRkZHqpZdeUocPH1YzZsxQ7dq1U40bN1be3t7Kzc1NzZ8/3/S8QYMGqTlz5qiBAweq5cuXm85v3bpVubm5qQoVKqhdu3Yppf4JWlWrVlVGo1EtW7ZMJSQkqCVLlqhPPvlEKaXU9evXM9XVunVrtX79+kzn2rVrp37++eenep87d+5Uc+fOfarnFkavv/66iouLy/V+8yKoaSoP73jQNM0SOAO0Aa4CB4AgpdTx+9rUAdYCLymlzuakX/dS1dTlm6dzXIdSijm/neOr7RnP6ePnzjtJR7k5cSLFOnbE7asvc9yXyN7p07fo2XMt4eHXsba2YNq0tgwblvVQthCicDt58mS211IVVNktGHu/pKQkihQpkscVCXPK6ntd07SDSinfp+kzT69RU0qla5o2HNhOxvIcPyiljmua9hkZ6XIT8BXgAKz5e974slKqU27VkGYwMnb9MVaFXUHT4ON2Xgxs5sG19xcDUKRundx6KQEsXRrOkCGbSUxMw9PTmVWrAmTpDSFEoZXTkAZISBNPJc/XUVNKbQW2PnBu/H2fv5BXrx2rT2PosoPsjriNrZWO6b18eKlmRmjQ/72fmF3denn18s+V5OR0hg7dwoIFRwAIDKzJ3LkdKFbMxsyVCSGEEAVXod1CKvJuEv0XHODsjQRKOlgz/7X6+JR3BCAtOpq0a9fQFS2KTRVPM1daOFhZ6bhwIQZbW0tmzmzHgAF1CsT6NEIIIUR+ViiD2l9XYhiwKIxbCSlUKe3AD/3qU975nyFn02haHR+0Jxi2FpkppdDr0ylSxAoLCx3LlnXjzh09NWuWNndpQgghRKFQ6ILatmPRvL3qMMlpRpp4lmBOn3oUt7PK1CbpYEZQK/KYjWjF48XHpzBkyBaioxPYsaMvFhY6XF2L4uqa/UrZQgiR3zzJTQEFlSogK/H/GwrS16LQBDWlFP/bdYH/bD2JUtCjXjn+07UW1pYP/4+XdPje9WkS1J7G4cNR9Oy5loiIO9jbW3Hs2A1q13Yxd1lCCPFUhg4dSsOGDQkICKB58+aPbFOyZEm2bdtmOt67dy9Xr14lICAgz+r67rvv+Ouvv/j222+fua9Vq1Zx/fp1RowYkQuVFXwDBgzgww8/zLThfH5VKP58SDcYGb/xOJ9vyQhp779YjS8DvB8Z0gwJiaScOg2WltjlcFNbkUEpxezZf9Kw4f+IiLiDt3cZwsIGSUgTQhRoDRs2ZNu2bVhYWHDr1i3CwsIATP/dt29fpm2YAH788UfTdlMpKSk4OjrSsmVLWrZsSc2aNfnkk09Mbbt164anpyc+Pj6P/HB3d2fUqFEAxMbGmj63tbXF2tqa7t274+3tja+vL76+vtSrV49mzZrl+P1dvnyZuXPnMmTIENO5MWPG0L17d9PxwoULCQwMNB2PHj2aCRMmAHD48GFq1KiBq6srY8aMyfHr5tSGDRsoX748Xl5eHPr70qTHGTx4MK6urnh4eLBp0yYgY0S0ePHiuLi44OLiQqNGjYCMr2fPnj1xd3enYcOGHD+esTrYuHHjeOONNzAYDLn+fnJbgR9RS0hJZ/jyQ/x2+ibWljqm9qhNp9quWbbX/3UEjEZsa9ZEZ2f3L1ZasMXEJDNgwCZ+/DHjh9WQIfWYNu1F7B6YVhZCiIKmbdu2xMfHP7bNvWmyjRs3MnnyZI4fP463tzczZszgtddew8fHh99++w3ICB5Hjx41PdfKyor58+fTsmXLR/a9cOFCTp06BYDBYGDNmjV8/fXX6HQ6LC0tM+21CRAZGUmTJk1y/P7Gjx/P1KlTsba2Np0LDg7m7Nmz2U75pqenExAQwLRp02jXrh3NmzenRYsWtG3bNsev/zg3btxg4MCBhIaGYmdnR8+ePU0B+VG2bNmCXq/n2rVrhIeH06ZNG6KiooiIiKBhw4Zs3749U/sPP/wQT09PVq9ezcaNG+nWrRunTp3Cw8ODjh07snjxYvr3758r7yWvFOgRtahYPT2+28tvp2/iVMSK5QP9HhvSAPRyfdpTmT//ED/+eJJixWxYvTqAb7/tICFNCFEouLi4MGzYMAwGA6VKlaJp06bcvXuXpk2botPpaNmyJdWrVwegc+fOrFmzBn9/fwIDAzlw4ACtW7fmypUreHp64unpyXvvvUdaWhqJiYlARlADOHLkiGlU7N7HvVE5S8uMcROdTmcKTnq9Hltb20fWnNPrq1JSUrh06RJ17/udFxcXR2RkJDVq1ODIkSOPff6uXbuws7Ojc+fOWFtb06VLF4KDg7NsP3z4cNOo1r2PV155Jcv227dvx9/fnxo1alCpUiXc3d05c+ZMlu1PnDhhGi3z9vZG0zQSExM5fPgwPj4+D7XfsmULw4YNAzL+7a5du0Z0dDQAr7/+OmvXrn3s+88PCuyI2rGrsQxYdIDrcSl4lLRnQb/6VCxpn+3z/rk+TRa6fRLvvNOQS5dieOedRlSq5GTucoQQBdmE4v/S68Rm22T58uVMnz4dR0dHduzYwYEDB/jpp58YNWoUv/zyC3aPmHmZOXMmw4cP56233uKtt97iyy+/RK/X06lTxlrtf/31F7t27aJbt26ZwkNaWhrVq1dn6dKlAFSsWNEU0B4lKSkJW1tbjEYjRqPxsW2zcv78+YdWyg8NDaVJkyZUq1aN4ODgTCHuQceOHct0HVf//v1NAfRRZs2axaxZs3JcX2RkJN7e3qbjChUqEBERQdWqVR/ZvmbNmkyePJnAwEB++eUXKlasSPHixTl8+DCrVq1i0aJFlCxZknnz5pkC3a1bt3Bzc+PixYukpaXh7OwMQIkSJUhKSspxreZSYEfUes7dy/W4FBpUdObHNxvnKKSp9HT0f4UDMqKWnVu3knjttQ1ERycAYGGhY+bM9hLShBCFSlBQEKtXrzaNUB05coTBgwdTrFgxmjVrhq+vL7a2tsyYMQOA06dPM336dD744AMuXLiAr68vQ4YMwcHBwXTNmZubG4MGDXpohOdRo2CPGxm7efMmpUuX5ujRo9SrV4969erh6+vLyy+/nOP3d/fuXZycMv/cDg4OpkWLFrRo0YKQkJDHPj8mJgYHBwfTcZkyZahUqVKOXz87BoOBYsWKmY7t7e2JiYnJsn3r1q25e/cu7du357333mPcuHGmuubNm0d0dDQTJkzgjTfeAKBPnz4EBQUxZcoU2rdvT6dOnbCx+Wch9rzcRjO3FNgRtaRUA118XJkS4I2NpUWOnpN86jQqKQmrCu5YliyZxxUWXL//fomgoHVcvRpPYmIqa9f2NHdJQojCJAcjXf82TdO4du0abdu2Zdq0aZw+fZoPPviAPXv2MGrUKNOF+FWrVuX69es4OTnh4+NDWFgYCQkJ2NraUrFiRYCHgtE9ycnJbN261RTgrl27RkpKSqY2aWlppKenA/Dnn3/SsWNHateuzV9//WVqExkZSdOmTXP0vkqUKMHt27cznQsODmbZsmXodDqSkpJIS0vL8vlWVlaZagwNDeXKlSv07dv3ke2HDx/+0HSiv78/S5YseWR7JyenTMFMr9c/9pq5SZMmERQUxEcffYRer6dJkybUrVvXdAMGQEBAAEOHDuXGjRtMmjQJLy8v9u/fz8mTJ1m8eHGWfedXBTaoDWzqwZiXvZ5oHZR7C90WkW2jHslgMDJ58i4++eQ3jEZF48blmTbtRXOXJYQQ/wpXV1eOHTtGyZIl+fTTT/Hz8yMlJYUtW7aYLsTXNA0nJydu3bpFbGwsw4YNo2HDhly+fJkvvvgCgCtXrtCmTZuH+m/atCl37tx5bA12dnZ8/PHHXLp0ibNnz+Ln5/dM76lSpUqZ7li9ceMG0dHR3Lx5E4DGjRuzb98+bG1tSU1NNbVLTU3FyckJT0/PTDcz/PHHH8TFxWX5ek869enr68vEiRNNx2FhYXTt2jXL9vv27WPo0KFAxteqcuXKhIeHs3XrVtMoWnJyMnfu3DFd7/faa68RFxdH586d8fX9Z1/06Ohoihf/l6bhn0GBnfpsVb30Ey9Wl3RIrk/LSnR0Ai++uJRx437FaFSMHt2E3357DXf3/P9NLIQQuWXPnj1MmjSJn376icqVK7Nr1y7TjQQA+/fvp3bt2vTq1Qu9Xk/fvn3p3LkzPj4+hISEsGLFCjp37swff/zB4cOHgZxNr91r4+DgwJtvvsmgQYN45513uHDhAlWqVKFOnTqmGxBefvnlHE/ZWVlZ4eXlxa5duwAICQmhfv36psf9/PwICQmhdu3a7N27l+joaOLj4/nll1+oU6cO/v7+XLhwgeDgYBISElizZg2tWrXK8dczOw0aNODs2bPMnz+f77//noiICBo0aJBlew8PDxYsWMCJEyf46aef2LlzJ9WrV2fVqlUsW7aMhIQEU8gu+ffMWWpqKtOnT+fzzz/P1NfcuXPp3bt3rr2XvFJgR9SelFLqnxG1ejKidr/4+BTq1p1LVFQCpUoVYcmSrrz4ouyBKoR4PixevNg0UnP58mVq1arFokWLqHXfWpvJycmkpqZSt25d9u7dS5EiRfDy8qJRo0a0bduWixcv8uKLL1KuXDkuXrxI7dq1cXHJWGMyLS2NoUOHUqRIkUe+/u3bt+nZM+MSk/j4ePr164dSihEjRqDT6Th79mym9pGRkZnCVnY+/fRTAgIC2Lp1K8HBwZlG6fz8/JgzZw6ffPIJI0eOxNfXF6PRSK9evUxLcNwbrbq3lEa7du1y/NrZ0TSNjRs3MmLECOLi4li3bh3W1tZcuXKFl19+mfDw8Eztx48fT79+/WjQoAE2NjZMnjwZDw8PZs+eTWBgIEOHDqVZs2asWLHC9Jz58+fTrFkzatasaToXHh7Orl27GD9+fK69lzyjlCpwH+VLVlW7zt5UTyLlyhV1olp1ddqvoTIajU/03OfBuHEhqlWrheratThzlyKEKGROnDhh7hKytGvXLtW+fXuVkpKiduzYoQIDA1XNmjVV+fLlVenSpVXRokWVlZWVAtS8efMyPbd69epKKaUSEhIynZ84caJasGCB6fjdd99Vx48fz7KGjRs3qqlTpyqllLp586YaPny4io+Pz7J9UlKSWr9+/RO9z507d6q5c+c+0XMKs5EjR6qrV6/mer9Zfa8DYeopM4+mCsAdDw9yL1VNrdi7myaeOb8hIHbTJq598CEOrVtTfs7sPKyuYIiMjOPq1Tj8/MoBGdenQcbdnUIIkZtOnjz50BIRQhRGWX2va5p2UCnl+4inZOu5+a38z0bscn3ali1n8PH5js6dV2ZafkNCmhBCCJG/PDe/mfWmGwme3+vTUlMNvPfeDjp0WMHt23p8fFywsHiyGzKEEEII8e95Lm4mMMTGknL2LJq1NbY1a5i7HLO4cOEugYHr+PPPq1hYaEya1Ib33muMTidBTQghhMivnougpv97LzPbmjXR3bcp7fNi8+Yz9O37I7GxKbi7F2fFiu40blze3GUJIYQQIhvPRVAzXZ9W7/ncNsrW1pK4uBQ6d67GDz90xtn54b3rhBBCiPsppZ54vdLCypxfi+fiGjXT9Wl1np+gFhubbPr8hRcqsXv366xf30tCmhBC5IIhQ4awefPmTOcaN27MiRMnHvu8Tz/9lH379uV6PYMHD36iHQGys2rVKmbOnJlr/RV0AwYM4PTp02Z57UIf1FRqKvqjRwGwq+OTTevCYeXKY1SoMJ2QkAumc40alZe/jIQQIgc+/fRTvv/++8e20ev1pm2l7rG0tMTKyirL56SlpTFr1izc3NyyrcHf3x8/Pz9atmyZ6cPPzw9/f/+H2tvY2GBvbw/A1atXKVGiBD4+Pnh4eDzxoq6XL19m7ty5pv1NAcaMGUP37t1NxwsXLiQwMNB0PHr0aCZMmADA4cOHqVGjBq6urowZM+aJXjsnNmzYQPny5fHy8uLQ3wMxjzN48GBcXV3x8PBg06ZNpvNvvfUW9vb2lClThuXLlwMQGxtLz549cXd3p2HDhhw/fhyAcePG8cYbb2AwGHL9/WSn0E99Jp84gUpJwbpyZSyz2Ci3sEhKSmPkyJ+ZPz9j25IffzxJ69YeZq5KCCHyt+bNmxMTE2MKXtHR0VhZWZnCWnp6OsWLFyc0NJSYmBiuX79OQkIC165dIzIykoCAACwsLDh27Bi9e/dGp9MRGBjIu+++C8DYsWNZu3YtaWlpxMXFZRm07t94/cEQeL/7H0tPT0cphU6nQ9M0kpOTsbS0pEKFChw6dIjp06dnu7/og8aPH8/UqVMzvU5wcDBnz57FaDQ+dtP09PR0AgICmDZtGu3ataN58+a0aNHCtMvBs7q3O0JoaCh2dnb07NmTsLCwLNtv2bIFvV7PtWvXCA8Pp02bNkRFRbFmzRpOnjzJuXPnOH36NB07dqR79+58+OGHeHp6snr1ajZu3Ei3bt04deoUHh4edOzYkcWLF9O/f/9ceS85VeiD2j/rpxXuac8TJ27Ss+cajh+/iY2NBf/970sMGvT8LkUihBA59fvvv2c6/uKLL3BxcaFfv34PtT19+jSLFi3i5MmTGAwGNE0zTWW2bNmS+fPn4+mZeQu+27dvM3bsWPr27ZtlDcWKFct07Ovri5WVFba2tpnOJycnk5aWlqn24cOHExkZibOzM9OmTWPDhg2ZwtTjgtWDUlJSuHTpEnXv+50ZFxdHZGQkNWrU4MiRI5kee9CuXbuws7Ojc+fOAHTp0oXg4OAsg9rw4cNZu3ZtpnP+/v4sWbLkke23b9+Ov78/NWpkrODg7u7OmTNnqFq16iPbnzhxgkaNGgHg7e2NpmkkJiZSvnx5FixYgIuLCy4uLmiaRkxMDFu2bDH9e3bu3Jm+ffsSHR1N2bJlef3113n11VclqOW2pMP31k8rnEFNKcXChUcYNmwren061aqVYNWqAGrXdjF3aUII8Ui1FtXKvlEuOPra0Ry3TUlJwcrKKttQ4+fnh5+fH/369aNv377cuHGDunXrotPpOH36NF27dsXGxgaj0cj7779P7969sbCwyPb1773uhg0b+PrrrylevPhDIe2etLQ0mjVrxvTp02ndujUnTpzA1dWVCRMm0K9fP27cuIGl5dP9ej9//vxDK+uHhobSpEkTqlWrRnBw8GOD2rFjx6hWrZrpuH///iQmJmbZftasWU90bV1kZCTe3t6m4woVKhAREZFlUKtZsyaTJ08mMDCQX375hYoVK1K8eHGaNm1qavPnn3/i7OxMmTJlALh16xZubm5cvHiRtLQ0nJ2dAShRogRJSUk5rjW3FOpr1JRS6A9lTAMW1js+Y2NTGD06GL0+nVde8SYsbJCENCGEeELDhg3D1dWVmjVrsnnzZubPn0/jxo2xs7Pjxo0bmdqmp6dz9+5dxowZQ3p6OmFhYYSFhREfH8/MmTOpXr06f/75Jz169Mj0vLNnz1KkSBF8fX3x9fWlZMmS/PDDD5nadOnShT/++AMXFxcqV65M9erVcXJywsnJierVq1O5cmVKly7NH3/8Qb16GbMme/bsISoqilWrVrFlyxZSU1OzDHnZuXv3Lk4PXCYUHBxMixYtaNGiBSEhIY99fkxMDA4ODqbjMmXKUKlSpaeq5VEMBkOm0Ud7e3tiYmKybN+6dWvu3r1L+/btee+99xg3btxDbUaPHs2HH34IQJ8+fQgKCmLKlCm0b9+eTp06YWNjY2prjm03C/WIWurFixju3MGiZEmsyhfOdcMcHW1ZtqwbkZFx9Ov3fNwsIYQo2J5kpOvfMn/+fLZu3cqqVatYtGgRAIsXL+bPP/+kdOnSpnbr1q1j5MiRGAwGZs2aRcmSJalbt65pBCs+Pp5bt27RoEEDZsyYkWnkxsrKiqpVq5quqerXr1+W16IdOnQIW1tbrK2tuXr1KgBubm6kpqaSmpqaqe306dOpXr069evXZ8SIESxfvvyhsJVTJUqU4Pbt25nOBQcHs2zZMnQ6HUlJSZmmXh9kZWVFSkqK6Tg0NJQrV65kOe37pFOfTk5OmYKZXq9/7CjopEmTCAoK4qOPPkKv19OkSRPq1q1ruqHj22+/JTU1lUGDBpnae3l5sX//fk6ePMnixYuz7PvfUqiDmmk0rW7dQnPHo1KKuXMPcueOno8/bgZkLL8hhBDi2bRv355p06axbds2ateuzbRp0x4aQWrUqBG///47/9/encfXeOUPHP+cRJZrSRr7TiwJQqeExIkOncAAACAASURBVFYxNKZ01L61tGLXGrT6axm1j2oVoxjLNBXLGBRt0epoFpSSVmypSpHYohIpQsgi2/n9cW9uhSyXZrvp9/165fW6z3PP89xz78nyzdm+c+fONQ+hvf3223Tv3h0XFxf279+Pv78/AQEBjwQQ9+/f59y5c7RubczNfenSJTp16pRjXdLT0+nduzdOTk7s378fMM6BS0hIYMuWLeZyhw8fJjw8nG7dutGgQQMOHz5MaGgodevWfaLPoEGDBkRERJiP4+LiiI2N5ddffwWMW5CEhobi6OiYLWBMTU3FxcWFRo0asWPHDvP5gwcPkpCQkOvrPe7QZ+vWrZk3b575OCwsjD59+uRaPjQ0lNdeew0Ag8FAw4YNCQ8Pp1atWpw8eZK5c+dy+PBhc1vZ2Njw6quvkpCQQK9evcxtBcZFJs7OzhbXtaCU6kAt6fgxAAylJBH7nTspjB69m23bzqAU9O3blCZNKhd3tYQQotTYtGkTnTt3pkyZMqxcudI8PylLzZo1sx3b2try/fffExQUlG0YM6dtOtzd3S2a46S1ZvHixeaeqazesdq1awPGxQ5Z0tLSWLJkCd988w1gHGoMCQmhffv2lrzdR9jZ2dG0aVMOHTpEx44dCQkJoU2bNubnvb29CQkJYeDAgbzxxhvExsZSrlw5AgMDWbx4MW3btmXcuHEEBwfj7e3Ntm3bstX39/Ly8uL8+fP4+/uTmZlJZGQkXl5euZZ3dXUlICCAxo0bExUVRVBQEAsXLuTmzZv07NmTlStX4uqafXeE1NRUli5dys6dO7OdX7NmDUOGDCmw92KpUh2o/TY/zfpXPx49+guDBm3n4sXbVKhgz7//3VOCNCGEKECnTp1i7dq11KhRg/Lly7NixQru379Pu3btzHuUgbGX6caNG+ZFArNnz2bJkiVkZGRw8+bNR3rS8hoqTEpKIiEhIVtgd+HCBd59910MBuMG5VlDn1k9XWlpaXh7e+Ps7IyPjw8Ae/bsISMjgytXrrB7925zcKSUIiMjg1WrVuHk5MTLL7+c7+cwZ84c+vfvz549e8wBVxZvb29WrlzJrFmzmDRpEq1btyYzM5NBgwaZV3bu2bOH0aNHm7fS6N69e76vaSmlFDt37mTixIkkJCSwY8cO7O3tiY6O5oUXXiA8PDxb+ZkzZzJ8+HC8vLxwcHBgwYIFuLq68tFHH3Ht2jXGjx/P+PHjAdi1axdeXl74+/vz7LPP0rx5c/N9wsPDOXTo0GPvSVcQVHFMjPu96lZx15uPfEeHRrkHKum3bnG+fQeUwYD7D9+j8tiEsCTTWrN0aSjvvBNEWlomrVrVYOvW/jRqVDH/i4UQogSIiIh4ZCVhSZGRkcHIkSM5cuQILVu2ZMSIEeaA4+jRoyxbtozg4GAqVKjAsWPHKF++PM8//zxKKbZu3ZptYvt7773HsmXL+Oc//5mt52X8+PF07tyZQYMGPfL6mzdvZvLkyQwdOpTFixcTFxdHv379cHJyMk/ZeXCOGhj/LmQFKVnz58aOHcszzzzD8ePH6dKli/n1v/76a0aPHk2ZMmXYtGkTHTp0sOhzCQ4OJioqyjx3649u8uTJvP3224/0qD4st+91pdQxrXXrHC7JV6kN1O4GB3P19QmU9fam3vp1RVe5AjZ79n7mzDkAwMSJXixc6IuDQ6nuCBVClDIlOVAD4z5nLi4ueU5KT0pKomzZskVYqyeT34a0onAVRqBWalsza6Nba5+fNnasJ+7ulfj880F89FF3CdKEEKKAVapUKd/gxhqCNHi8zW2FdSi1LZqViN3aMhJkZmo2bjxFRkYmADVqVOCnn16jd+8mxVwzIYQQQhS1UhmoZaakkPzTT6AUhmesZ2+x69fv0b37Jl555Qv+8Y/fUprY2pbKZhJCCCFEPkrlOFrK6dOQloaDuzu2FSoUd3UsEhJykZdf/ozY2HtUrlwWL69axV0lIYQQQhSzUhmomROxW0HaqIyMTObOPcC8ed+iNXTqVI///rcvtWo55X+xEEKIUk9rXWo2bf+9/oifRakcU8uan2ZoWbIDtfj4ZLp23cDcucZhzpkzOxEc/IoEaUIIUQLExsZy5MgR8/GtW7c4e/YsISEhrFmzhldffZWDBw8+ct3mzZu5cOFCgdRh69atLF++vEDuVRqMHDmSs2fPFnc1ilSpC9R0ZiZJJ6wjEbuTkwM2Norq1csTFPQKc+b8mTJlSl2TCCFEiTV79mymTp0KwE8//UTFihXNgcDVq1fp378/p06dAqB///68/vrrfPrpp1y/fp1OnTpl20cN4MaNG4wfP55Vq1aZz/n4+NCsWTOeeeYZ7O3tuXz5skV1u3LlCmvWrGHcuHHmc9OnT6dfv37m43Xr1jF48GDz8dSpU5k9ezYAJ06cwMPDg5o1azJ9+vTH+FQs88UXX1CnTh2aNm3KcVMHSV7Gjh1LzZo1cXV1ZdeuXebzM2fOpGrVqnTt2pVbt25lu2b06NGsW7fOfDxjxgxGjx5NRkZGgb2Pkq7UDX2mRkWRmZBAmerVsctnY7rikJaWwb17qbi4GLC1teG//+2HUlCtWvnirpoQQvxhpaWlMWzYMObPn4+7uztgzCu5fPlyPvnkE5YtW4ajoyNLly7Fzc0t1/tMmDCByZMnEx4ezpAhQwgICMDOzo49e/bw1FNP4ePjQ7169Syq08yZM1m0aFG2xO3BwcGcP38+3/3S0tPT6d+/P0uWLKF79+506tQJHx8f82a+v1dW1oEDBw5gMBgYOHCgOdl8Tr766iuSk5O5du0a4eHhdO3alZiYGPbu3cvnn39OZGQkX331Fe+++y4rV640v/8NGzZk26TX1dWVnj17smHDBvz8/ArkvZR0pa77JumBROwlzeXLt/HxWUf//tvM229Ur15egjQhhChmc+bMoWbNmuZ0Qln69u3LsmXLAONmstu2bWP16tXmr+XLl3Pp0iUAPvjgA27evEnHjh3ZvHkz6enpjBo1ijJljH0iQUFBFgdK9+/f5/Lly7R64G9ZQkICV69excPDg5MnT+Z5/aFDhzAYDPTq1Qt7e3t69+5NcHBwruUnTJhA9erVs30NGzYs1/J79+7F19cXDw8PGjRoQN26dTl37lyu5c+cOUO7du0AePrpp1FKkZiYyGeffcaECRNwcnJi8ODB5qHkM2fOcOvWrRyzOYwYMYLt27fn+f5Lk1LXo5ZsTsResgK1nTt/xs9vJ/HxKdSu7UR0dAL16z9V3NUSQogiF9GkaLIUNP05wqJyP/zwA99++222QOObb77hzTffRGvN7t27adCgAXfv3sVgMFCmTBnmzJnD66+/jrOzM0opdu3axdatW9m3bx99+vRh0qRJbNiwgbi4OMaOHQvA7t27GT58uEV1unDhwiM73B84cIAOHTrg7u5OcHBwtiDuYadPnzb3DAL4+fmRmJiYa/kVK1awYsUKi+oGxmHhp59+2nxcr149IiMjc+1tbN68OQsWLGDw4MEEBgZSv359nJ2duXr1KqNGjQKMeTzt7OxITEykadOmrFixIsfPq1KlShYlty8tSm+PWgmZn3b/fjqTJ/+P3r23Eh+fwl//6sbJk2MlSBNCiBJi37591KlTh88++8x8rlu3bpw+fRoXFxeyUi3GxMQwYcIERo0aRcWKFRk8eDDjx4+nXr16vPjiiwQFBVGhQgX+/ve/U6VKFQwGQ7Zhzqeeeooff/zRojrFx8fj4uKS7VxwcDA+Pj74+PgQEhKS5/W3b9+mfPnfRmuqVatGgwYNLHptS2RkZGSbn1euXDlu376da/kuXboQHx9Pjx49eOutt5gxY0aO9ylbtix37tzJd2WnNaa/fFKlqkctLS6OtOhobMqVwyGPOQRFJTLyFoMGbef48RjKlLHhgw+e44032v7hlhYLIcSDLO3pKioDBgxg/PjxDBkyhNdffx2DwZDteVtbW2JiYsjMzDTPF8spUBgzZgynTp3C1taW9PR0Ll++zE8//WR+fsGCBTRv3pwhQ4ZQpUqVPOtUqVIlbt68me1ccHAwmzZtwsbGhqSkJNLS0nK93s7Ojvv375uPDxw4QHR0NEOHDs2x/IQJEx4ZTvT19WXjxo05lndxcckWmCUnJ+c5Z+69997jpZdeYtq0aSQnJ9OhQwdatWr12Pf5IypVn0ayqTfN8MwzKFvbYq4NbNlymuPHY6hf/ym++24Eb77ZToI0IYQoYRo0aMCf//xnGjZsyMcff5xjmfXr19OjRw/zcWZmJpmZmdnKbN++nePHj/Pzzz8zbtw43nzzzWxDgWXLluUvf/kLu3fvtqhOERG/BbRxcXHExsby66+/cv36dVq0aEFoaCiOjo6kpqaay6WmpmIwGGjUqFG2LUIOHjxIeHh4rq+3YsUKYmNjs33lFqSBcaHFg1uXhIWFUbt27VzLh4aG0qxZMwAMBgMNGzYkPDw8232SkpK4ePEilSpVyuOTMW6b4uzsnGeZ0qRU9aglmeenlYxE7NOmdSQzUzNxojdPPeVY3NURQgiRh+nTpzNmzBjGjh2Lg4OD+fyNGzfw9/cnKCjIfC4jIyPHLSIGDhxIzZo1OXHiBN999535vNaaDz/8kMDAQJ577rl862JnZ0fTpk05dOgQHTt2JCQkhDZt2pif9/b2JiQkhIEDB/LGG28QGxtLuXLlCAwMZPHixbRt25Zx48YRHByMt7c327Zt4/3333/Sj+YRXl5enD9/Hn9/fzIzM4mMjMTLyyvX8q6urgQEBNC4cWOioqIICgpi4cKFuLm50blzZ55++mk+//xzunTpgp2dXZ6vvWbNGoYMGVJg76WkK5U9amU9PYvl9X/++Qa+vhuJjb0HGHN0zpzpI0GaEEJYgR49elCtWjXWrl0LwEcffcSJEyfo378/Y8aMITk52TzcuGjRIqpVq0Zqaip37twx32PLli38+OOPNG/e3DyEmpGRgVIKZ2dnPv7442z7oOVlzpw5TJs2jbt375oDrize3t4EBwfTtGlTJk2aROvWrXF3d+e5556jW7duODk5sWfPHqZMmYKbmxu9evWie/fuBfVRoZRi586dfPrpp6xdu5YdO3Zgb29PdHR0tkUGWWbOnElSUhJeXl4MHz6cBQsW4OrqSsOGDVm5ciV///vfuXbtWr4LGsLDwzl06FCOq0FLK2WNE/LqVnHXm498R4dGlc3nMhMTOetl/CZ2P/oDNmXLFmmd1q8/yWuv7SEpKY2RI1vi7/9ikb6+EEKUVBEREY+sYCzpjh07xsiRI/nmm29ISUnB39+f/fv3c/HiRfOQZ0pKCrdv32bLli0MGDDAfO2NGzeYN28eCxcuxMHBAR8fH9avX0/9+vUfux7BwcFERUUxZsyYgnprVm3y5Mm8/fbb1CyB+6RC7t/rSqljWuvWT3LPUhOoJYaGcmW4H44eHrjuKLr9Ve7dS+X11/ewYYNx5+qXX27BqlUvUKGCQz5XCiHEH4M1Bmpg3DQ2aw+0nJ5LT08nIyMDg8EgE+AFUDiBWqmZo5Z0zDQ/rQi35QgPv87Agds4e/YmBkMZ/vWvHgwf/owsGBBCiFIgtyAt67m8nheioJSa77LkIs5IcP36Pdq1+4SkpDQ8PKqwdWt/PDyqFslrCyGEEOKPoVQEajojg2RTOg1Dy6IJ1KpVK8+UKe2IibnLRx91p2zZvFepCCGEEEI8rlIRqN0/d47MxETsatfGrlrh9WodO3aNxMQ0OnUy7jQ9Z05nGeYUQgghRKEpFbMfk44fBwovbZTWmmXLvqddu08YOHAb168bt9+QIE0IIYQQhalU9KglHzMGaoUx7HnrVjIjRuxk586zAAwY0AxnZ9kXTQghxG9u376Ng4MDZcqUyXfD1uKitZYOBhNr+ixKR4/aicJJxH7kSDQtW65h586zODs7sGPHQJYv74GjY6mIb4UQQpikpKQwYMAA7ty5Q3JyMj179uTXX3+1+PpFixbx4YcfsnTpUiZMmPBIeqnWrVvz888/m4979erF+fPnWbBggXmD3SxJSUmkp6dnO/dgmqjo6Ghat368nR62bt3K8uXLH+ua0mzkyJGcPXu2uKthEasP1NKuXSM9JgYbJyfsGzYssPuuXh3Gs88GcOXKHby8anHixFj69rW+fYCEEELkb/ny5cTExHDixAm+//57atSowZYtW9i/fz/79u3j8OHDeV5vMBgoU6YMU6ZM4fbt2xw6dCjb8w4ODjg4OLBx40Y6duzI4cOHGTZsGB9//DELFy6kbdu2XLp0CTDu4u/h4UHdunXp2LEjjo6OvPzyyxw8eJCXX37ZfC9LXblyhTVr1jBu3DjzuenTp2fLkLBu3ToGDx5sPp46dSqzZ88G4MSJE3h4eFCzZk2mT59u8eta6osvvqBOnTo0bdqU46apTHkZO3YsNWvWxNXVlV27dj3y/KxZs8x1B9i7dy/du3enZ8+e/PDDDwDMmDGD0aNH55gGrKSx+q6hpKxtOVq2RBXghoONG1dEa3jrrXbMn98Ve/viT/IuhBCi4J04cYIZM2bwzjvvEBYWRkpKCtu3b8fNzY2wsDAAqlatSvv27QFjtoDJkydnC5bi4uLIyMjgs88+Iy0tDT8/P6Kiorh79y7R0dGkpKRw/vx5fH19GTZsGPPnz2fUqFEEBQVRsWJFnn/+efO9Fi1ahKenJ5GRkUybNo1GjRqxbds2QkNDsbOzw8bG5rGG7WbOnMmiRYuwt7c3nwsODub8+fNkZmbmuVlveno6/fv3Z8mSJXTv3p1OnTrh4+NDt27dLH79vMTFxTFq1CgOHDiAwWBg4MCB5s88J1999RXJyclcu3aN8PBwunbtSkxMjHlPu59//pn333+fadOmAXDhwgUmTZrEpk2buHz5Mn379iU6OhpXV1d69uzJhg0b8PPzK5D3UlisPlBLNidi//3DntHRd6hTxxmArl0bcPbsBBo1qvi77yuEEKJkunnzJkOGDKFy5coEBgZib29Peno6SUlJfPnllwAkJCRw9OhR8zWJiYl4enqybt0687ldu3YRGBjI8uXLycjIwNbW+M/9lStXWLp0KZGRkaxdu5YRI0bQr18/7OzsCAwMBCAtLY1PP/2UgIAALl26xJ49ewgLC+POnTusXbuWe/fusWrVKurUqfPY7+/+/ftcvnyZVg/8jUxISODq1at4eHhw8uTJbM897NChQxgMBnr16gVA7969CQ4OzjVQmzBhAtu3Z88O5Ovry8aNG3Msv3fvXnx9ffHw8ACgbt26nDt3Djc3txzLnzlzhnbt2gHw9NNPo5QiMTERZ2dntNaMGTOGvn37mstfv36d5cuX4+npiaenJ2PGjOHevXtUqFCBESNG8Morr0igVtjMPWq/Y35aRkYm8+cfZN68b9m7dyhdurgCSJAmhBCF4F/jQorkdV5f3SXfMklJScyfP5/ly5czatQonnrqKRITE5k4cSJvvfUWYAykEhIScHFxAXJe8V+zZk3i4uIA8PPzY8CAAfTs2RMPDw8++OADAgICePfdd2nevDnTpk1j9OjR5mvj4+M5ZsquY2Njg4ODA1evXsXNzQ1HR0eUUjg4OJjTVj2OCxcuPJLS6MCBA3To0AF3d3eCg4PzDNROnz6Nu7u7+djPz4/ExMRcy69YsSLfxOoPunr1arYk7vXq1SMyMjLXQK158+YsWLCAwYMHExgYSP369XF2Nnaw+Pv7U79+fbp06WIeRs4K6tLS0li9ejWenp5UqFABgEqVKpGUlGRxXYuLVc9Ry7h7l/tnz4KdHY7Nmz/RPWJi7tKt23+YNWs/GRmZHD8eU8C1FEIIUVLVqVPHPFcrOjqaS5cuceXKFdLT07l06RKXLl3i4sWL3L9/33yN1povvviCJk2aUKdOHQYNGkStWrWIjIzk7t277Nq1i2bNmpnLb968Ga01r776KmFhYWitqV+/vvmrdu3aZOXdrlu3Li+++CIRERG8/fbbDB48GEdHR0aMGEHbtm0ZO3ZsvsOVD4qPjzcHmFmCg4Px8fHBx8eHkJC8g+bbt29Tvnx583G1atVo0KCBRa9tiYyMDJycnMzH5cqV4/bt27mW79KlC/Hx8fTo0YO33nqLGTNmAMaes8WLF7N06dIcr3vnnXeYMmUKf/vb37Kdt4Z851bdo5Z88hRojcHDAxvHx98yIzAwiqFDPycuLpGqVcvxn//0wde34BYkCCGEeJQlPV1FLTExkd27d2Nvb4/Wmtq1a5uH8NLS0vjll19YvHgxYOyFGzx4MKtXr+aLL74gKCiIGjVqEBMTw+LFixkyZAgNTYvbEhMTCQgIwNPTk+nTp/Pll1/SsGFDZs+ezeXLl6latSqOjo54eXkBxqFYLy8vXnrpJerWrUtKSgpdu3YlPT2d6tWrU7ZsWXx9fS1Ocl+pUiVu3ryZ7VxwcDCbNm3CxsaGpKQk0tLScr3ezs4uW5B64MABoqOjGTp0aI7lH3fo08XFJVtglpycnGcQ+t577/HSSy8xbdo0kpOT6dChA61atWLKlCnMnTuXihVzHglbsmQJfn5+dO3alaioKHOvmjWw6kAt6Qnnp6WnZzJr1j4WLDiE1tC1qyv/+U9fqlcvn//FQgghSpW0tDQmTpxIv379KFu2LDt27OD69eu89tpr5jKpqanmvbdiYmLMvUAJCQlUrlwZAG9vb1atWkV4eLj5uk8++YSePXsSGBjIn/70J1544QVsbGywsbHhxRdfZNGiRbi5uZGRkUFGRgaVKlXC39+f9u3bk5mZiaOjI507d+bdd9/l/fffx8nJiQ8//BBvb2+L3luDBg2IiIgwH8fFxREbG2veeqR9+/aEhobi6OiYbQuQ1NRUXFxcaNSoETt27DCfP3jwIAkJCbm+3uMOfbZu3Zp58+aZj8PCwujTp0+u5UNDQ83tYjAYaNiwIeHh4Xz11Vfs37+fiRMnkpycTEZGBvHx8fj5+VGhQgUaNmxIixYtqFmzJleuXMHDw4PY2FjzsGlJZtWB2m+J2Fs+1nW3biXj738CpRRz53Zm2rSO2Npa9SiwEEKIJ3Dv3j3ziss1a9Zw4sQJDAYDtra2/O1vf6N169bmDWy3bt1KrVq1OHbsGM8++yx37tzh5MmTNGzYkISEBC5evEizZs2oVq0a+/btw83Njddee43k5GTzwoFPPvmETZs2AXD27Fk6depEkyZNsLW1ZeHChXh6etKiRQvatGmDvb09NjY23Lp1i5SUFIKCggDjAoEtW7aYJ+Dnxc7OjqZNm3Lo0CE6duxISEgIbdq0MT/v7e1NSEgIAwcO5I033iA2NpZy5coRGBjI4sWLadu2LePGjSM4OBhvb2+2bdvG+++/X2Cfv5eXF+fPn8ff35/MzEwiIyPNvYs5cXV1JSAggMaNGxMVFUVQUBALFy7k7t275jLr1q3j0qVLzJ49mw0bNhAQEMDXX39NVFQUsbGxNG7cGDC295AhQwrsvRQarbXVfdWp7KYPRcToiD89o8+4N9FpN29qS2RmZpofh4Rc0AcOXLLoOiGEEE/uzJkzxV2FPN26dUt//vnnul+/fnrq1Kl69erVeubMmXrr1q26ffv2+r333tNHjhzRKSkpOikpSVepUkVHRkbq559/Xrdp00bv3btXt2rVSq9cuVJ369ZNL168WA8cOFCvWbPG/BodOnTQFy9e1Fpr/euvv+oxY8boL7/8Uu/cuVMPGzZM37hxI9f6bdy4UU+fPv2J319sbKzu2LGjTkhI0KNGjdKzZ882P7d582b97LPPaq21XrBgga5Vq5auUaOGnjx5srnMDz/8oP/0pz/pGjVq6BkzZjxxPXITERGhfX19tbe3tz5y5IjWWusrV67oFi1aPFL2l19+0b6+vrpcuXK6YsWKetWqVY+UCQgI0LNmzdJaG//uT5gwQVetWlV7eHjoffv2aa21PnXqlH7uueeyxQUFIbfvdSBMP2HMU+xB15N81anspo989a0+495ER/7l+Xw/uPv30/Ubb/xPz5q1L9+yQgghClZJDtTu3r2rO3furKdOnaovXTL+875y5UpzoJKamqo/++wzPWLECH3x4kW9fv163bt3b/P1+/fv1/Xq1dM7d+7UWmt9+fJl7ebmpm1tbfWFCxfM5by8vPS5c+f0mDFjdNeuXfXRo0fNz+3Zs0c3atRI79mzRx85ckS7ublpT09P81fjxo11vXr1sp1r1qyZXr9+vcXvMygoKFvg+Ec3adIk/csvvxT4fQsjUFPaClY8PKxuFXf92TsTKee/Aud+fak5f36uZS9ciGfw4O0cPXoNBwdboqImUquWU67lhRBCFKyIiAiLJ79bg9TUVPPmsZmZmdy8eZMqVaqYn09LS+P69evUrl37kWtTUlJwzGHxW2pqKnZ2dlaTf1LkLLfvdaXUMa314+X9MrHaiVl2Z34EoGweCwm2bz9Dy5ZrOHr0GvXqObN//3AJ0oQQQvwuD+7wb2Njky1IA+O8sJyCNCDHIC3rnhKkiZxY7WKCMhHGQC2nFZ8pKem8+eZeVq0ypqHo06cJn3zyIi4uhiKtoxBCCCNtWjEpRGlVWCOUVtujZhN/C9uKFbGvX/+R56ZMMQZp9va2LF/enR07BkqQJoQQxcTOzo7k5OTiroYQhSotLc2cc7QgWW2PGoChVcsc/0N7991OnDp1nWXLutOqVY1iqJkQQogsVatW5ZdffqFWrVoYDAbpWROlTmZmJtevXy+UfdmsOlAr29I47JmYmMrKlUd588122NraUKNGBQ4e9JNfBkIIUQJkbQ577dq1PHfBF8KalStXzrz5cUGy7kDNsxWnT8cxcOA2IiJukJKSzowZPkDOSXOFEEIUDycnp2w5HYUQlin0OWpKqeeVUmeVUpFKqak5PO+glNpqev57pVR9S+6baWfPf0JTadPmYyIibtC0aWX69Ck9y7+FEEIIIQo1UFNK2QL/AroDzYAhSqlmDxUbwl1+wgAACJxJREFUCcRrrRsB/wQ+yO++Wmv+705Xxoz/mpSUdPz8nuHo0dE0b161oN+CEEIIIUSxKeweNS8gUmt9QWudCmwBej1Uphew3vR4O9BV5TNuGRufxNfRzpQrZ8fGjX1Yu7YX5crZ53WJEEIIIYTVKew5arWA6AeOrwLeuZXRWqcrpe4AlYAbud3U1lZRv7YjX34zEnf3gp+4J4QQQghREljNYgKl1BhgjOnwfuSVqaebNHlkypuwDpXJIxAXJZq0nXWT9rNu0n7Wy/1JLyzsQO0XoM4Dx7VN53Iqc1UpVQZwBm4+fCOt9b+BfwMopcKeNGeWKH7SftZL2s66SftZN2k/66WUCnvSawt7jtpRoLFSylUpZQ8MBnY9VGYX8KrpcX8gRFtjpnghhBBCiAJWqD1qpjlnE4C9gC2wVmv9k1JqLhCmtd4FfAJsVEpFArcwBnNCCCGEEH94hT5HTWu9B9jz0LmZDzxOAQY85m3/XQBVE8VH2s96SdtZN2k/6ybtZ72euO2UjDIKIYQQQpRMhZ6ZQAghhBBCPJkSHagVVvopUTQsaL83lVJnlFLhSqlgpVS94qineFR+bfdAuX5KKa2UkpVoJYgl7aeUGmj6+ftJKfXfoq6jyJkFvzfrKqX2KaVOmH539iiOeopHKaXWKqXilFKnc3leKaWWmdo2XCnVypL7lthArbDST4miYWH7nQBaa62fxpiVYmHR1lLkxMK2QylVAZgEfF+0NRR5saT9lFKNgWlAB621BzC5yCsqHmHhz967wKda65YYF9+tLNpaijysA57P4/nuQGPT1xhglSU3LbGBGoWUfkoUmXzbT2u9T2udZDoMxbjPnih+lvzsAczD+M9RSlFWTuTLkvYbDfxLax0PoLWOK+I6ipxZ0nYacDI9dgauFWH9RB601t9i3L0iN72ADdooFHhKKVUjv/uW5EAtp/RTtXIro7VOB7LST4niZ0n7PWgk8HWh1khYKt+2M3XZ19Faf1WUFRMWseRnzw1wU0p9p5QKVUrl1Qsgio4lbTcbGKqUuopxR4W/FU3VRAF43L+LgBWlkBKll1JqKNAa8Cnuuoj8KaVsgCXA8GKuinhyZTAOv3TG2JP9rVKqhdb6drHWSlhiCLBOa71YKdUO4z6kzbXWmcVdMVE4SnKP2uOknyKv9FOiWFjSfiilngOmAy9qre8XUd1E3vJruwpAc2C/UuoS0BbYJQsKSgxLfvauAru01mla64vAOYyBmyhelrTdSOBTAK31EcARYw5QUfJZ9HfxYSU5UJP0U9Yt3/ZTSrUE1mAM0mSOTMmRZ9tpre9orStrretrretjnF/4otb6iXPZiQJlye/OLzD2pqGUqoxxKPRCUVZS5MiStrsCdAVQSjXFGKj9WqS1FE9qF/CKafVnW+CO1jomv4tK7NCnpJ+ybha234dAeWCbaQ3IFa31i8VWaQFY3HaihLKw/fYC3ZRSZ4AM4P+01jIaUcwsbLspwMdKqTcwLiwYLh0UJYNSajPGf4Aqm+YQzgLsALTWqzHOKewBRAJJgJ9F95X2FUIIIYQomUry0KcQQgghxB+aBGpCCCGEECWUBGpCCCGEECWUBGpCCCGEECWUBGpCCPEQpVQl096MQghRrCRQE0JYJaWUhym5+IPnbEz7T/1ee4FGD917hlJqqgX1mmXKtiGEEL+b/McohLBW/weEKaWOA2eBBsBQYLoybsxXQWvdQil1HriOMaOCxpgJIwDjxqFZqmutaz9wfN/0hVLqHxg39b0PpD9cCaXUJxh39c9K4VMHKKuUGmU6tgVuaK37/P63LIT4o5FATQhhdZRSFYBuwGtAf611Z6XUduCM1rqDUsoROGAqnoZxE8rRGNPMpQJ7tNbDTfcqA0SZHivAHmNA94JS6j8Y89DuBJoAmVllHkh5Nh9I0lrHKqXcAH/AF7DRWicrpTwwbiorhBCPTYY+hRDWaASQoLVOApqaAqo2gKdSai2wHGPABb/1dPXDmDopp12+s87VBb40PW6OMfF8S2AVMAl4EwgDtj5wbQfgf0qpBsBS4O/AGGCdaWj2U9M9hBDisUmPmhDCqiilqmFMvZJiOnUOGGd6bA/8YHruwWHKikBTrfXPSqkHhzgf1gSIAFoBG4FRGFP3vKCUegtI11ovffACrfVGpdQ54FmgJrAYY+7FQab7jdFaf/ek71cI8ccmPWpCCGvzFDCX3wKxp4H/Af/FOG/ta9PjkAeuiQd+Vkr91XT8V6VUmFIqDOP8syxVgUOmx79gTKK8JK/KmIZhB2GcJ7cUOIlxmHUfxoTZ7yulWjz+2xRCCMn1KYSwUkqpMK11a6XUfq11Z9M5T6Avxt6waVrrV5VSp4FnMPZu/QuYBwx9aI5apNa6/gP3PmQqc0kptQDoDlTCOER6E/hRa/2KUsoWOGa6bwrwNsZky3cwBofRwHZgiNZ6UmF+HkKI0kmGPoUQpYJSygCcwtjbdho4/FCRKMD9ce+rtZ4GTMtl6FMBf9Zax5uON5pWicYCa4BMrXWGUuqYUqqM1vqRVaNCCJEXCdSEENbO0zSEeUdr3VUpdR0YCfR/oExHjKsxgwv4tV8Apiql7j9wzhXjytL+AMZFothi7HXbUsCvL4Qo5SRQE0JYKxvTVhnHHhj6LINxO456gF1WOYw9bf211ieVUr5Ad9PwZhb10L3LALZKKTtAP9wTZtpUN11rvRPj1h0PPvcPIFZrvaIg3qQQ4o9NFhMIIaxVecAh60Ap1QXjQoAfgGHAXqVUe1OZu1rrk6ai6cA6rXVHrXVHwAfjAoAH2WFcQToC+M4U1PUG+psef0fuW27Y8VuQKIQQv4ssJhBClAqm3jQ7rXWy6bgskKzll5wQwopJoCaEEEIIUULJ0KcQQgghRAklgZoQQgghRAklgZoQQgghRAklgZoQQgghRAklgZoQQgghRAn1/4/FOrbsJlqMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, precision_recall_curve\n",
    "from sklearn.pipeline import Pipeline\n",
    "import glob\n",
    "import time\n",
    "import seaborn as sns\n",
    "from typing import List, Dict, Any, Tuple, Optional\n",
    "import warnings\n",
    "import psutil  # 用于内存监控\n",
    "\n",
    "# 忽略警告\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning, module=\"matplotlib\")\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"sklearn.utils.fixes\")\n",
    "\n",
    "class AutismClassifier:\n",
    "    \"\"\"自闭症分类器 - 用于处理眼动和表情数据分析的完整解决方案\"\"\"\n",
    "    \n",
    "    def __init__(self, asd_path: str, td_path: str, max_frames: int = 2000, random_seed: int = 42, font_path: str = None):\n",
    "        \"\"\"初始化分类器\"\"\"\n",
    "        self.asd_path = asd_path\n",
    "        self.td_path = td_path\n",
    "        self.max_frames = max_frames\n",
    "        self.random_seed = random_seed\n",
    "        self.scaler = StandardScaler()\n",
    "        self.models = {}\n",
    "        self.results = []\n",
    "        self.best_params = {}\n",
    "        self.font = None\n",
    "        \n",
    "        # 设置字体\n",
    "        self.setup_font(font_path)\n",
    "    \n",
    "    def setup_font(self, font_path: str = None) -> None:\n",
    "        \"\"\"设置中文字体\"\"\"\n",
    "        try:\n",
    "            if font_path and os.path.exists(font_path):\n",
    "                # 使用指定的字体文件\n",
    "                self.font = FontProperties(fname=font_path)\n",
    "                plt.rcParams[\"font.family\"] = self.font.get_name()\n",
    "                print(f\"[字体设置] 使用自定义字体: {self.font.get_name()}\")\n",
    "            else:\n",
    "                # 尝试系统中常见的中文字体\n",
    "                available_fonts = [\"SimHei\", \"WenQuanYi Micro Hei\", \"Heiti TC\", \n",
    "                                  \"Microsoft YaHei\", \"SimSun\", \"WenQuanYi Micro Hei\"]\n",
    "                \n",
    "                for font_name in available_fonts:\n",
    "                    try:\n",
    "                        self.font = FontProperties(family=font_name)\n",
    "                        plt.rcParams[\"font.family\"] = font_name\n",
    "                        print(f\"[字体设置] 使用系统字体: {font_name}\")\n",
    "                        break\n",
    "                    except:\n",
    "                        continue\n",
    "                \n",
    "                # 如果没有找到任何中文字体，使用默认字体\n",
    "                if self.font is None:\n",
    "                    print(\"[字体设置] 未找到可用的中文字体，将使用默认字体\")\n",
    "                    plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "            \n",
    "            # 确保负号正确显示\n",
    "            plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"[字体设置错误] {e}，将使用默认字体\")\n",
    "            plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "    \n",
    "    def load_data(self) -> Tuple[pd.DataFrame, np.ndarray]:\n",
    "        \"\"\"加载并处理数据\"\"\"\n",
    "        # 验证数据路径\n",
    "        if not os.path.exists(self.asd_path):\n",
    "            raise FileNotFoundError(f\"错误：ASD路径 {self.asd_path} 不存在！\")\n",
    "        if not os.path.exists(self.td_path):\n",
    "            raise FileNotFoundError(f\"错误：TD路径 {self.td_path} 不存在！\")\n",
    "            \n",
    "        # 获取所有数据文件\n",
    "        asd_files = glob.glob(os.path.join(self.asd_path, \"*.csv\"))\n",
    "        td_files = glob.glob(os.path.join(self.td_path, \"*.csv\"))\n",
    "        print(f\"[数据加载] ASD样本：{len(asd_files)}，TD样本：{len(td_files)}\")\n",
    "        \n",
    "        # 提取特征\n",
    "        X, y = [], []\n",
    "        for file in asd_files:\n",
    "            feat = self.extract_features(file)\n",
    "            if feat is not None:\n",
    "                X.append(feat)\n",
    "                y.append(1)  # ASD标签为1\n",
    "                \n",
    "        for file in td_files:\n",
    "            feat = self.extract_features(file)\n",
    "            if feat is not None:\n",
    "                X.append(feat)\n",
    "                y.append(0)  # TD标签为0\n",
    "                \n",
    "        # 转换为DataFrame\n",
    "        if not X:\n",
    "            raise ValueError(\"没有有效的特征数据，请检查CSV文件格式和内容\")\n",
    "            \n",
    "        X_df = pd.DataFrame(X).astype(np.float32)  # 立即转换为float32以减少内存占用\n",
    "        y = np.array(y, dtype=np.int8)  # 使用int8存储标签\n",
    "        print(f\"[数据维度] 特征矩阵：{X_df.shape}\")\n",
    "        \n",
    "        # 内存监控\n",
    "        self._print_memory_usage(\"加载数据后\")\n",
    "        \n",
    "        return X_df, y\n",
    "    \n",
    "    def extract_features(self, file_path: str) -> Optional[Dict[str, Any]]:\n",
    "        \"\"\"从CSV文件中提取特征\"\"\"\n",
    "        try:\n",
    "            # 使用chunksize分块读取大文件（如果需要）\n",
    "            df_chunks = pd.read_csv(file_path, chunksize=self.max_frames)\n",
    "            df = next(df_chunks).astype(np.float32)  # 只取第一块并转换为float32\n",
    "            \n",
    "            # 验证数据有效性\n",
    "            if len(df) < 2:  # 确保有足够数据计算差分\n",
    "                print(f\"[警告] 文件 {file_path} 数据行数不足，跳过\")\n",
    "                return None\n",
    "                \n",
    "            # 处理可能的无效值\n",
    "            df = df.replace([np.inf, -np.inf], np.nan).dropna()\n",
    "            if len(df) == 0:\n",
    "                print(f\"[警告] 文件 {file_path} 处理后无有效数据，跳过\")\n",
    "                return None\n",
    "                \n",
    "            # 基础特征\n",
    "            required_cols = [\"Gaze_X\", \"Gaze_Y\", \"Expression\"]\n",
    "            valid_cols = [col for col in required_cols if col in df.columns]\n",
    "            \n",
    "            if not valid_cols:\n",
    "                print(f\"[警告] 缺少关键列：{file_path}\")\n",
    "                return None\n",
    "                \n",
    "            features = {}\n",
    "            for col in valid_cols:\n",
    "                # 基础统计特征（简化版：仅保留核心特征）\n",
    "                features[f\"{col}_mean\"] = df[col].mean()\n",
    "                features[f\"{col}_std\"] = df[col].std()\n",
    "                features[f\"{col}_median\"] = df[col].median()\n",
    "                \n",
    "                # 可选：保留部分分位数特征\n",
    "                features[f\"{col}_q25\"] = df[col].quantile(0.25)\n",
    "                features[f\"{col}_q75\"] = df[col].quantile(0.75)\n",
    "                \n",
    "                # 动态特征 (一阶差分统计量)\n",
    "                diff = df[col].diff().dropna()\n",
    "                if not diff.empty:\n",
    "                    features[f\"{col}_diff_mean\"] = diff.mean()\n",
    "                    features[f\"{col}_diff_std\"] = diff.std()\n",
    "            \n",
    "            # 特征间的交互项\n",
    "            if \"Gaze_X\" in valid_cols and \"Gaze_Y\" in valid_cols:\n",
    "                features[\"gaze_distance_mean\"] = np.sqrt(\n",
    "                    df[\"Gaze_X\"].mean()**2 + df[\"Gaze_Y\"].mean()**2\n",
    "                )\n",
    "                features[\"gaze_variance_ratio\"] = features[\"Gaze_X_std\"] / (features[\"Gaze_Y_std\"] + 1e-10)\n",
    "                \n",
    "            return features\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"[错误] 处理文件 {file_path}: {e}\")\n",
    "            return None\n",
    "    \n",
    "    def preprocess_data(self, X: pd.DataFrame) -> np.ndarray:\n",
    "        \"\"\"数据预处理 - 标准化特征\"\"\"\n",
    "        return self.scaler.fit_transform(X)\n",
    "    \n",
    "    def split_data(self, X: pd.DataFrame, y: np.ndarray, test_size: float = 0.2) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n",
    "        \"\"\"划分训练集和测试集\"\"\"\n",
    "        X_train, X_test, y_train, y_test = train_test_split(\n",
    "            X, y, test_size=test_size, stratify=y, random_state=self.random_seed\n",
    "        )\n",
    "        print(f\"[数据集划分] 训练集：{len(X_train)}，测试集：{len(X_test)}\")\n",
    "        \n",
    "        # 标准化数据\n",
    "        X_train_scaled = self.preprocess_data(X_train)\n",
    "        X_test_scaled = self.scaler.transform(X_test)\n",
    "        \n",
    "        # 内存监控\n",
    "        self._print_memory_usage(\"数据划分后\")\n",
    "        \n",
    "        return X_train_scaled, X_test_scaled, y_train, y_test\n",
    "    \n",
    "    def define_models(self) -> None:\n",
    "        \"\"\"定义要使用的模型及其参数搜索空间\"\"\"\n",
    "        # 降低模型复杂度，减少内存消耗\n",
    "        self.models = {\n",
    "            \"随机森林\": {\n",
    "                \"model\": RandomForestClassifier(random_state=self.random_seed, n_jobs=1),  # 单线程训练\n",
    "                \"params\": {\n",
    "                    \"n_estimators\": [50, 100],  # 减少树的数量\n",
    "                    \"max_depth\": [None, 10],    # 限制树的深度\n",
    "                    \"min_samples_split\": [2, 5]\n",
    "                }\n",
    "            },\n",
    "            \"逻辑回归\": {\n",
    "                \"model\": LogisticRegression(random_state=self.random_seed, max_iter=1000),\n",
    "                \"params\": {\n",
    "                    \"C\": [0.1, 1.0],  # 减少参数搜索范围\n",
    "                    \"solver\": ['liblinear', 'lbfgs']\n",
    "                }\n",
    "            },\n",
    "            \"支持向量机\": {\n",
    "                \"model\": SVC(random_state=self.random_seed, probability=True),\n",
    "                \"params\": {\n",
    "                    \"C\": [0.1, 1.0],  # 减少参数搜索范围\n",
    "                    \"kernel\": ['rbf', 'linear'],\n",
    "                    \"gamma\": ['scale']\n",
    "                }\n",
    "            },\n",
    "            \"K近邻\": {\n",
    "                \"model\": KNeighborsClassifier(),\n",
    "                \"params\": {\n",
    "                    \"n_neighbors\": [3, 5],  # 减少参数搜索范围\n",
    "                    \"weights\": ['uniform', 'distance']\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    \n",
    "    def hyperparameter_tuning(self, X_train: np.ndarray, y_train: np.ndarray, cv: int = 5) -> None:\n",
    "        \"\"\"超参数调优（单线程运行，避免内存过载）\"\"\"\n",
    "        print(\"\\n=== 超参数调优 ===\")\n",
    "        \n",
    "        # 使用StratifiedKFold确保类别平衡\n",
    "        skf = StratifiedKFold(n_splits=cv, shuffle=True, random_state=self.random_seed)\n",
    "        \n",
    "        # 内存监控\n",
    "        self._print_memory_usage(\"调优开始前\")\n",
    "        \n",
    "        for name, model_info in self.models.items():\n",
    "            print(f\"\\n[调优开始] {name}\")\n",
    "            start_time = time.time()\n",
    "            \n",
    "            grid_search = GridSearchCV(\n",
    "                model_info[\"model\"],\n",
    "                model_info[\"params\"],\n",
    "                cv=skf,  # 使用分层K折交叉验证\n",
    "                scoring='f1',\n",
    "                n_jobs=1,  # 单线程运行，避免内存竞争\n",
    "                verbose=0\n",
    "            )\n",
    "            \n",
    "            grid_search.fit(X_train, y_train)\n",
    "            \n",
    "            self.models[name][\"best_model\"] = grid_search.best_estimator_\n",
    "            self.best_params[name] = grid_search.best_params_\n",
    "            \n",
    "            print(f\"[调优完成] {name} - 最佳参数: {grid_search.best_params_}\")\n",
    "            print(f\"[调优完成] {name} - 最佳F1分数: {grid_search.best_score_:.4f}\")\n",
    "            print(f\"[调优时间] {name}: {time.time() - start_time:.2f}秒\")\n",
    "    \n",
    "    def train_and_evaluate(self, X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray, y_test: np.ndarray) -> None:\n",
    "        \"\"\"训练并评估所有模型\"\"\"\n",
    "        print(\"\\n=== 模型训练与评估 ===\")\n",
    "        \n",
    "        # 内存监控\n",
    "        self._print_memory_usage(\"训练开始前\")\n",
    "        \n",
    "        # 添加集成模型\n",
    "        estimators = [(name, model_info[\"best_model\"]) for name, model_info in self.models.items()]\n",
    "        self.models[\"集成模型\"] = {\n",
    "            \"best_model\": VotingClassifier(estimators, voting='soft', n_jobs=1),  # 单线程训练集成模型\n",
    "            \"params\": {}\n",
    "        }\n",
    "        \n",
    "        for name, model_info in self.models.items():\n",
    "            print(f\"\\n[训练开始] {name}\")\n",
    "            start_time = time.time()\n",
    "            \n",
    "            model = model_info[\"best_model\"]\n",
    "            model.fit(X_train, y_train)\n",
    "            \n",
    "            train_time = time.time() - start_time\n",
    "            y_pred = model.predict(X_test)\n",
    "            y_prob = model.predict_proba(X_test)[:, 1] if hasattr(model, \"predict_proba\") else None\n",
    "            \n",
    "            # 计算评估指标\n",
    "            cm = confusion_matrix(y_test, y_pred)\n",
    "            tn, fp, fn, tp = cm.ravel()\n",
    "            \n",
    "            accuracy = (tp + tn) / (tp + tn + fp + fn)\n",
    "            precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "            recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "            f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n",
    "            \n",
    "            # 保存结果\n",
    "            result = {\n",
    "                \"算法\": name,\n",
    "                \"准确率\": round(accuracy, 4),\n",
    "                \"精确率\": round(precision, 4),\n",
    "                \"召回率\": round(recall, 4),\n",
    "                \"F1分数\": round(f1, 4),\n",
    "                \"训练时间\": round(train_time, 2),\n",
    "                \"模型\": model,\n",
    "                \"y_pred\": y_pred,\n",
    "                \"y_prob\": y_prob\n",
    "            }\n",
    "            \n",
    "            self.results.append(result)\n",
    "            print(f\"[训练完成] {name} - F1分数: {f1:.4f}\")\n",
    "    \n",
    "    def visualize_results(self, y_test: np.ndarray) -> None:\n",
    "        \"\"\"可视化模型评估结果\"\"\"\n",
    "        # 创建结果DataFrame\n",
    "        results_df = pd.DataFrame(self.results)\n",
    "        \n",
    "        # 打印性能对比表\n",
    "        print(\"\\n=== 多算法性能对比表 ===\")\n",
    "        print(results_df[[\"算法\", \"准确率\", \"精确率\", \"召回率\", \"F1分数\", \"训练时间\"]].to_string(index=False))\n",
    "        \n",
    "        # 确保字体设置\n",
    "        if self.font is not None:\n",
    "            plt.rcParams[\"font.family\"] = self.font.get_name()\n",
    "        else:\n",
    "            plt.rcParams[\"font.family\"] = [\"SimHei\", \"WenQuanYi Micro Hei\", \"Heiti TC\", \"sans-serif\"]\n",
    "        \n",
    "        # 绘制性能对比图 - 增加图形尺寸\n",
    "        plt.figure(figsize=(16, 10))\n",
    "        metrics = [\"准确率\", \"精确率\", \"召回率\", \"F1分数\"]\n",
    "        \n",
    "        for i, metric in enumerate(metrics, 1):\n",
    "            plt.subplot(2, 2, i)\n",
    "            ax = sns.barplot(x=\"算法\", y=metric, data=results_df)\n",
    "            \n",
    "            # 在柱状图上方添加数值标签\n",
    "            for p in ax.patches:\n",
    "                ax.annotate(f\"{p.get_height():.4f}\", \n",
    "                            (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                            ha='center', va='center', \n",
    "                            fontproperties=self.font if self.font is not None else None,\n",
    "                            fontsize=12,\n",
    "                            xytext=(0, 5), \n",
    "                            textcoords='offset points')\n",
    "            \n",
    "            plt.title(f\"{metric}对比\", fontproperties=self.font if self.font is not None else None, fontsize=14)\n",
    "            plt.xlabel(\"算法\", fontproperties=self.font if self.font is not None else None, fontsize=12)\n",
    "            plt.ylabel(metric, fontproperties=self.font if self.font is not None else None, fontsize=12)\n",
    "            plt.ylim(0, 1.05)\n",
    "            plt.xticks(rotation=15, ha='right', fontproperties=self.font if self.font is not None else None)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(\"model_performance_comparison.png\", dpi=300, bbox_inches='tight')\n",
    "        print(\"[可视化] 已保存模型性能对比图\")\n",
    "        \n",
    "        # 绘制ROC曲线\n",
    "        self._plot_roc_curve(y_test)\n",
    "        \n",
    "        # 绘制混淆矩阵\n",
    "        self._plot_confusion_matrix(y_test)\n",
    "    \n",
    "    def _plot_roc_curve(self, y_test: np.ndarray) -> None:\n",
    "        \"\"\"绘制ROC曲线\"\"\"\n",
    "        plt.figure(figsize=(10, 8))\n",
    "        \n",
    "        for result in self.results:\n",
    "            if result[\"y_prob\"] is not None:\n",
    "                fpr, tpr, _ = roc_curve(y_test, result[\"y_prob\"])\n",
    "                roc_auc = auc(fpr, tpr)\n",
    "                plt.plot(fpr, tpr, lw=2, \n",
    "                         label=f'{result[\"算法\"]} (AUC = {roc_auc:.3f})')\n",
    "        \n",
    "        plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.05])\n",
    "        plt.xlabel('假阳性率', fontproperties=self.font if self.font is not None else None, fontsize=12)\n",
    "        plt.ylabel('真阳性率', fontproperties=self.font if self.font is not None else None, fontsize=12)\n",
    "        plt.title('受试者工作特征曲线', fontproperties=self.font if self.font is not None else None, fontsize=14)\n",
    "        plt.legend(loc=\"lower right\", prop=self.font if self.font is not None else None)\n",
    "        plt.savefig(\"roc_curve.png\", dpi=300, bbox_inches='tight')\n",
    "        print(\"[可视化] 已保存ROC曲线图\")\n",
    "    \n",
    "    def _plot_confusion_matrix(self, y_test: np.ndarray) -> None:\n",
    "        \"\"\"绘制混淆矩阵\"\"\"\n",
    "        n_models = len(self.results)\n",
    "        cols = min(3, n_models)  # 每行最多显示3个图\n",
    "        rows = (n_models + cols - 1) // cols  # 计算需要的行数\n",
    "        \n",
    "        plt.figure(figsize=(5 * cols, 4 * rows))\n",
    "        \n",
    "        for i, result in enumerate(self.results, 1):\n",
    "            plt.subplot(rows, cols, i)\n",
    "            cm = confusion_matrix(y_test, result[\"y_pred\"])\n",
    "            \n",
    "            sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
    "                        xticklabels=[\"TD\", \"ASD\"], \n",
    "                        yticklabels=[\"TD\", \"ASD\"],\n",
    "                        annot_kws={\"fontproperties\": self.font if self.font is not None else None})\n",
    "            \n",
    "            plt.title(f'{result[\"算法\"]} 混淆矩阵', fontproperties=self.font if self.font is not None else None)\n",
    "            plt.xlabel('预测类别', fontproperties=self.font if self.font is not None else None)\n",
    "            plt.ylabel('实际类别', fontproperties=self.font if self.font is not None else None)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(\"confusion_matrices.png\", dpi=300, bbox_inches='tight')\n",
    "        print(\"[可视化] 已保存混淆矩阵图\")\n",
    "    \n",
    "    def run_pipeline(self) -> None:\n",
    "        \"\"\"运行完整的分类流程\"\"\"\n",
    "        print(\"=== 自闭症分类分析开始 ===\")\n",
    "        \n",
    "        # 加载数据\n",
    "        X, y = self.load_data()\n",
    "        \n",
    "        # 划分数据集\n",
    "        X_train, X_test, y_train, y_test = self.split_data(X, y)\n",
    "        \n",
    "        # 定义模型\n",
    "        self.define_models()\n",
    "        \n",
    "        # 超参数调优\n",
    "        self.hyperparameter_tuning(X_train, y_train)\n",
    "        \n",
    "        # 训练与评估\n",
    "        self.train_and_evaluate(X_train, X_test, y_train, y_test)\n",
    "        \n",
    "        # 可视化结果\n",
    "        self.visualize_results(y_test)\n",
    "        \n",
    "        print(\"\\n=== 自闭症分类分析完成 ===\")\n",
    "        print(\"分析结果已保存为图片文件\")\n",
    "    \n",
    "    def _print_memory_usage(self, message: str) -> None:\n",
    "        \"\"\"打印当前内存使用情况\"\"\"\n",
    "        process = psutil.Process(os.getpid())\n",
    "        mem_info = process.memory_info()\n",
    "        print(f\"[内存监控] {message}: {mem_info.rss / 1024 / 1024:.2f} MB\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # 参数设定\n",
    "    DATA_PATH_ASD = \"ASD/ASD\"\n",
    "    DATA_PATH_TD = \"TD/TD\"\n",
    "    MAX_FRAMES = 2000\n",
    "    RANDOM_SEED = 42\n",
    "    \n",
    "    # 使用绝对路径或验证文件存在性\n",
    "    FONT_PATH = \"./SimHei.ttf\"\n",
    "    \n",
    "    # 验证字体文件\n",
    "    if not os.path.exists(FONT_PATH):\n",
    "        print(f\"警告：字体文件不存在: {FONT_PATH}\")\n",
    "        FONT_PATH = None  # 自动回退到系统字体\n",
    "    \n",
    "    # 创建并运行分类器\n",
    "    classifier = AutismClassifier(\n",
    "        asd_path=DATA_PATH_ASD,\n",
    "        td_path=DATA_PATH_TD,\n",
    "        max_frames=MAX_FRAMES,\n",
    "        random_seed=RANDOM_SEED,\n",
    "        font_path=FONT_PATH\n",
    "    )\n",
    "    \n",
    "    # 运行完整流程\n",
    "    classifier.run_pipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.6/site-packages (7.0.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install psutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24.2\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24.2\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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